Tongliang Liu

According to our database, Tongliang Liu authored at least 322 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Regularly Truncated M-Estimators for Learning With Noisy Labels.

Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu

IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

ProtoSimi: label correction for fine-grained visual categorization.

Jialiang Shen, Yu Yao, Shaoli Huang, Zhiyong Wang, Jing Zhang, Ruxing Wang, Jun Yu, Tongliang Liu

Mach. Learn., April, 2024

Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification.

Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu

IEEE Trans. Multim., 2024

Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization.

Yulong Yang, Chenhao Lin, Qian Li, Zhengyu Zhao, Haoran Fan, Dawei Zhou, Nannan Wang, Tongliang Liu, Chao Shen

IEEE Trans. Inf. Forensics Secur., 2024

Negative Label Guided OOD Detection with Pretrained Vision-Language Models.

Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

CoRR, 2024

Few-Shot Adversarial Prompt Learning on Vision-Language Models.

Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu

CoRR, 2024

Tackling Noisy Labels with Network Parameter Additive Decomposition.

Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu

CoRR, 2024

NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation.

Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han

CoRR, 2024

Mitigating Label Noise on Graph via Topological Sample Selection.

Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu

CoRR, 2024

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.

Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han

CoRR, 2024

Robust Training of Federated Models with Extremely Label Deficiency.

Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han

CoRR, 2024

Federated Causal Discovery from Heterogeneous Data.

Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

CoRR, 2024

Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision.

Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu

CoRR, 2024

FedImpro: Measuring and Improving Client Update in Federated Learning.

Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu

CoRR, 2024

Discovery of the Hidden World with Large Language Models.

Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

CoRR, 2024

Neural auto-designer for enhanced quantum kernels.

Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu

CoRR, 2024

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization.

Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang

CoRR, 2024

Prompt-based Multi-interest Learning Method for Sequential Recommendation.

Xue Dong, Xuemeng Song, Tongliang Liu, Weili Guan

CoRR, 2024

E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning.

Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Exploring Channel-Aware Typical Features for Out-of-Distribution Detection.

Rundong He, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu, Yongshun Gong

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Dynamics-aware loss for learning with label noise.

Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu

Pattern Recognit., December, 2023

A Parametrical Model for Instance-Dependent Label Noise.

Shuo Yang, Songhua Wu, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu

IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

Recent Advances for Quantum Neural Networks in Generative Learning.

Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., October, 2023

On exploring node-feature and graph-structure diversities for node drop graph pooling.

Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu

Neural Networks, October, 2023

Understanding How Pretraining Regularizes Deep Learning Algorithms.

Yu Yao, Baosheng Yu, Chen Gong, Tongliang Liu

IEEE Trans. Neural Networks Learn. Syst., September, 2023

Handling Open-Set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation.

Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan

IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

An Optimal Transport Analysis on Generalization in Deep Learning.

Jingwei Zhang, Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., June, 2023

Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels.

Xiaobo Xia, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, Tongliang Liu

IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering.

Jie Ma, Jun Liu, Yaxian Wang, Junjun Li, Tongliang Liu

IEEE Trans. Neural Networks Learn. Syst., 2023

Trustable Co-Label Learning From Multiple Noisy Annotators.

Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge

IEEE Trans. Multim., 2023

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation.

Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han

Trans. Mach. Learn. Res., 2023

FedDAG: Federated DAG Structure Learning.

Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell

Trans. Mach. Learn. Res., 2023

Type-I Generative Adversarial Attack.

Shenghong He, Ruxin Wang, Tongliang Liu, Chao Yi, Xin Jin, Renyang Liu, Wei Zhou

IEEE Trans. Dependable Secur. Comput., 2023

One Shot Learning as Instruction Data Prospector for Large Language Models.

Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

CoRR, 2023

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance.

Linghao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu

CoRR, 2023

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation.

Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman H. Khan, Lina Yao, Tongliang Liu, Kun Zhang

CoRR, 2023

Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning.

Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu

CoRR, 2023

Coreset Selection with Prioritized Multiple Objectives.

Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Tongliang Liu

CoRR, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han

CoRR, 2023

DeepInception: Hypnotize Large Language Model to Be Jailbreaker.

Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han

CoRR, 2023

InstanT: Semi-supervised Learning with Instance-dependent Thresholds.

Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu

CoRR, 2023

Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization.

Zhuo Huang, Muyang Li, Li Shen, Jun Yu, Chen Gong, Bo Han, Tongliang Liu

CoRR, 2023

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation.

Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han

CoRR, 2023

IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models.

Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu

CoRR, 2023

On the Over-Memorization During Natural, Robust and Catastrophic Overfitting.

Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu

CoRR, 2023

On the Onset of Robust Overfitting in Adversarial Training.

Chaojian Yu, Xiaolong Shi, Jun Yu, Bo Han, Tongliang Liu

CoRR, 2023

Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training.

Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Tongliang Liu, Wenping Wang

CoRR, 2023

Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm.

Shikun Li, Xiaobo Xia, Hansong Zhang, Shiming Ge, Tongliang Liu

CoRR, 2023

Gradient constrained sharpness-aware prompt learning for vision-language models.

Liangchen Liu, Nannan Wang, Dawei Zhou, Xinbo Gao, Decheng Liu, Xi Yang, Tongliang Liu

CoRR, 2023

Regularly Truncated M-estimators for Learning with Noisy Labels.

Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu

CoRR, 2023

Continual Learning From a Stream of APIs.

Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, Dacheng Tao

CoRR, 2023

ShadowNet for Data-Centric Quantum System Learning.

Yuxuan Du, Yibo Yang, Tongliang Liu, Zhouchen Lin, Bernard Ghanem, Dacheng Tao

CoRR, 2023

Channel-Wise Contrastive Learning for Learning with Noisy Labels.

Hui Kang, Sheng Liu, Huaxi Huang, Tongliang Liu

CoRR, 2023

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation.

Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han

CoRR, 2023

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels.

Hui Kang, Sheng Liu, Huaxi Huang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu

CoRR, 2023

Why do CNNs excel at feature extraction? A mathematical explanation.

Vinoth Nandakumar, Arush Tagade, Tongliang Liu

CoRR, 2023

Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer.

Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Tianshuo Xu, Xiaoshuai Sun, Tongliang Liu, Rongrong Ji, Dacheng Tao

CoRR, 2023

Why can neural language models solve next-word prediction? A mathematical perspective.

Vinoth Nandakumar, Peng Mi, Tongliang Liu

CoRR, 2023

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision.

Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu

CoRR, 2023

Advancing Counterfactual Inference through Quantile Regression.

Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang

CoRR, 2023

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds.

Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu

CoRR, 2023

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning.

Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama

CoRR, 2023

Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation.

Bochao Liu, Shiming Ge, Pengju Wang, Liansheng Zhuang, Tongliang Liu

CoRR, 2023

Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths.

Yulong Yang, Chenhao Lin, Qian Li, Chao Shen, Dawei Zhou, Nannan Wang, Tongliang Liu

CoRR, 2023

Fairness Improves Learning from Noisily Labeled Long-Tailed Data.

Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu

CoRR, 2023

Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation.

Jiren Mai, Fei Zhang, Junjie Ye, Marcus Kalander, Xian Zhang, Wankou Yang, Tongliang Liu, Bo Han

CoRR, 2023

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation.

Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning.

Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

An Efficient Dataset Condensation Plugin and Its Application to Continual Learning.

Enneng Yang, Li Shen, Zhenyi Wang, Tongliang Liu, Guibing Guo

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training.

Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David S. Doermann, Mingchen Gao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation.

Yexiong Lin, Yu Yao, Xiaolong Shi, Mingming Gong, Xu Shen, Dong Xu, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization.

Runqi Lin, Chaojian Yu, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

InstanT: Semi-supervised Learning with Instance-dependent Thresholds.

Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning.

Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Label-free Scene Understanding by Vision Foundation Models.

Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, Wenping Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping.

Yingbin Bai, Zhongyi Han, Erkun Yang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions.

Wenjie Xuan, Shanshan Zhao, Yu Yao, Juhua Liu, Tongliang Liu, Yixin Chen, Bo Du, Dacheng Tao

Proceedings of the 31st ACM International Conference on Multimedia, 2023

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities.

Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, Dacheng Tao

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability.

Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han

Proceedings of the International Conference on Machine Learning, 2023

Exploring Model Dynamics for Accumulative Poisoning Discovery.

Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han

Proceedings of the International Conference on Machine Learning, 2023

Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration.

Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2023

Phase-aware Adversarial Defense for Improving Adversarial Robustness.

Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2023

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2023

A Universal Unbiased Method for Classification from Aggregate Observations.

Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen

Proceedings of the International Conference on Machine Learning, 2023

Detecting Out-of-distribution Data through In-distribution Class Prior.

Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

Proceedings of the International Conference on Machine Learning, 2023

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation.

Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han

Proceedings of the International Conference on Machine Learning, 2023

Evolving Semantic Prototype Improves Generative Zero-Shot Learning.

Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

Proceedings of the International Conference on Machine Learning, 2023

Combating Exacerbated Heterogeneity for Robust Models in Federated Learning.

Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning.

Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Out-of-distribution Detection with Implicit Outlier Transformation.

Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Symmetric Pruning in Quantum Neural Networks.

Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Mosaic Representation Learning for Self-supervised Visual Pre-training.

Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, Tongliang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond.

Yong Lin, Renjie Pi, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Contextual Convolutional Networks.

Shuxian Liang, Xu Shen, Tongliang Liu, Xian-Sheng Hua

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style.

Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unicom: Universal and Compact Representation Learning for Image Retrieval.

Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Late Stopping: Avoiding Confidently Learning from Mislabeled Examples.

Suqin Yuan, Lei Feng, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

ALIP: Adaptive Language-Image Pre-training with Synthetic Caption.

Kaicheng Yang, Jiankang Deng, Xiang An, Jiawei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Holistic Label Correction for Noisy Multi-Label Classification.

Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples.

Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Point-Query Quadtree for Crowd Counting, Localization, and More.

Chengxin Liu, Hao Lu, Zhiguo Cao, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels.

Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering.

Dongting Hu, Zhenkai Zhang, Tingbo Hou, Tongliang Liu, Huan Fu, Mingming Gong

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

HumanMAC: Masked Motion Completion for Human Motion Prediction.

Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting.

Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency.

Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization.

Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning.

Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

A machine learning approach for predicting human shortest path task performance.

Shijun Cai, Seok-Hee Hong, Xiaobo Xia, Tongliang Liu, Weidong Huang

Vis. Informatics, 2022

On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier.

Jingwei Zhang, Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2022

TWGAN: Twin Discriminator Generative Adversarial Networks.

Zhaoyu Zhang, Mengyan Li, Haonian Xie, Jun Yu, Tongliang Liu, Chang Wen Chen

IEEE Trans. Multim., 2022

LR-SVM+: Learning Using Privileged Information with Noisy Labels.

Zhengning Wu, Xiaobo Xia, Ruxin Wang, Jiatong Li, Jun Yu, Yinian Mao, Tongliang Liu

IEEE Trans. Multim., 2022

NoiLin: Improving adversarial training and correcting stereotype of noisy labels.

Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama

Trans. Mach. Learn. Res., 2022

Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation.

Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Quan Zhou, Tongliang Liu, Zongyuan Ge

IEEE Trans. Medical Imaging, 2022

Label Propagated Nonnegative Matrix Factorization for Clustering.

Long Lan, Tongliang Liu, Xiang Zhang, Chuanfu Xu, Zhigang Luo

IEEE Trans. Knowl. Data Eng., 2022

Quantum Differentially Private Sparse Regression Learning.

Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao

IEEE Trans. Inf. Theory, 2022

Exploring Language Hierarchy for Video Grounding.

Xinpeng Ding, Nannan Wang, Shiwei Zhang, Ziyuan Huang, Xiaomeng Li, Mingqian Tang, Tongliang Liu, Xinbo Gao

IEEE Trans. Image Process., 2022

Laplacian Welsch Regularization for Robust Semisupervised Learning.

Jingchen Ke, Chen Gong, Tongliang Liu, Lin Zhao, Jian Yang, Dacheng Tao

IEEE Trans. Cybern., 2022

COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment.

Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang

Pattern Recognit., 2022

Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration.

Shuo Yang, Songhua Wu, Tongliang Liu, Min Xu

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation.

Xu Yang, Cheng Deng, Tongliang Liu, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval.

Hao Wang, Cheng Deng, Tongliang Liu, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation.

Chen Gong, Qizhou Wang, Tongliang Liu, Bo Han, Jane You, Jian Yang, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Learning from Noisy Pairwise Similarity and Unlabeled Data.

Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama

J. Mach. Learn. Res., 2022

A Machine Learning Approach for Predicting Human Preference for Graph Layouts.

Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu

J. Graph Algorithms Appl., 2022

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations.

Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao

CoRR, 2022

Strength-Adaptive Adversarial Training.

Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu

CoRR, 2022

Towards Lightweight Black-Box Attacks against Deep Neural Networks.

Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian

CoRR, 2022

Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos.

Lianyang Ma, Yu Yao, Tao Liang, Tongliang Liu

CoRR, 2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.

Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han

CoRR, 2022

MSR: Making Self-supervised learning Robust to Aggressive Augmentations.

Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu

CoRR, 2022

Robust Weight Perturbation for Adversarial Training.

Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu

CoRR, 2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts.

Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu

CoRR, 2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs.

Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

CoRR, 2022

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu

CoRR, 2022

Counterfactual Fairness with Partially Known Causal Graph.

Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Pluralistic Image Completion with Gaussian Mixture Models.

Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Watermarking for Out-of-distribution Detection.

Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Lightweight Black-Box Attack Against Deep Neural Networks.

Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning.

Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE.

Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models.

Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization.

De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning.

Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks.

Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.

Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MAPS: a dataset for semantic profiling and analysis of Android applications.

Amirmohammad Pasdar, Young Choon Lee, Seok-Hee Hong, Tongliang Liu

Proceedings of the MobiArch '22: Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture, 2022

Nonlinear Multi-Model Reuse.

Yong Luo, Ling-Yu Duan, Yan Bai, Tongliang Liu, Yihang Lou, Yonggang Wen

Proceedings of the 24th IEEE International Workshop on Multimedia Signal Processing, 2022

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification.

Xin Jin, Tianyu He, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua

Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data.

Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.

Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Robust Weight Perturbation for Adversarial Training.

Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Modeling Adversarial Noise for Adversarial Training.

Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2022

Improving Adversarial Robustness via Mutual Information Estimation.

Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2022

Understanding Robust Overfitting of Adversarial Training and Beyond.

Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2022

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network.

Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu

Proceedings of the International Conference on Machine Learning, 2022

To Smooth or Not? When Label Smoothing Meets Noisy Labels.

Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu

Proceedings of the International Conference on Machine Learning, 2022

Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors.

Joshua Y. Kim, Tongliang Liu, Kalina Yacef

Proceedings of the International Conference on Multimodal Interaction, 2022

Reliable Adversarial Distillation with Unreliable Teachers.

Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Adversarial Robustness Through the Lens of Causality.

Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Exploiting Class Activation Value for Partial-Label Learning.

Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama

Proceedings of the Tenth International Conference on Learning Representations, 2022

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning.

Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao

Proceedings of the Tenth International Conference on Learning Representations, 2022

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu

Proceedings of the Tenth International Conference on Learning Representations, 2022

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data.

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Proceedings of the Tenth International Conference on Learning Representations, 2022

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.

Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng

Proceedings of the Tenth International Conference on Learning Representations, 2022

Unleashing the Potential of Adaptation Models via Go-getting Domain Labels.

Xin Jin, Tianyu He, Xu Shen, Songhua Wu, Tongliang Liu, Jingwen Ye, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua

Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

Mutual Quantization for Cross-Modal Search with Noisy Labels.

Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Exploring Set Similarity for Dense Self-supervised Representation Learning.

Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

CRIS: CLIP-Driven Referring Image Segmentation.

Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Selective-Supervised Contrastive Learning with Noisy Labels.

Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation.

Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation.

De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC.

Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Fair Classification with Instance-dependent Label Noise.

Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Learning and Mining with Noisy Labels.

Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices.

Amirmohammad Pasdar, Young Choon Lee, Tongliang Liu, Seok-Hee Hong

Proceedings of the 22nd IEEE International Symposium on Cluster, 2022

2021

HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking.

Jia Shao, Bo Du, Chen Wu, Mingming Gong, Tongliang Liu

IEEE Trans. Image Process., 2021

KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization.

Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu

IEEE Trans. Image Process., 2021

Orthogonal Deep Neural Networks.

Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning.

Chen Gong, Hong Shi, Tongliang Liu, Chuang Zhang, Jian Yang, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2021

A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection.

Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao

Int. J. Comput. Vis., 2021

Federated Causal Discovery.

Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell

CoRR, 2021

Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors.

Joshua Yee Kim, Tongliang Liu, Kalina Yacef

CoRR, 2021

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification.

Xin Jin, Tianyu He, Zhiheng Yin, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Xian-Sheng Hua, Zhibo Chen

CoRR, 2021

Modelling Adversarial Noise for Adversarial Defense.

Dawei Zhou, Nannan Wang, Tongliang Liu, Bo Han

CoRR, 2021

Kernel Mean Estimation by Marginalized Corrupted Distributions.

Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu

CoRR, 2021

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels.

Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

CoRR, 2021

KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation.

Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Kwok-Wai Cheung

CoRR, 2021

Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training.

Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Jun Yu, Xiaoyu Wang, Tongliang Liu

CoRR, 2021

Understanding (Generalized) Label Smoothing when Learning with Noisy Labels.

Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu

CoRR, 2021

Instance Correction for Learning with Open-set Noisy Labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama

CoRR, 2021

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?

Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama

CoRR, 2021

Estimating Instance-dependent Label-noise Transition Matrix using DNNs.

Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu

CoRR, 2021

Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation.

Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, Zongyuan Ge

CoRR, 2021

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data.

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han

CoRR, 2021

Understanding the Interaction of Adversarial Training with Noisy Labels.

Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama

CoRR, 2021

Robust Dual Recurrent Neural Networks for Financial Time Series Prediction.

Jiayu He, Matloob Khushi, Nguyen Hoang Tran, Tongliang Liu

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Instance-dependent Label-noise Learning under a Structural Causal Model.

Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Probabilistic Margins for Instance Reweighting in Adversarial Training.

Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Confident Anchor-Induced Multi-Source Free Domain Adaptation.

Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation.

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Understanding and Improving Early Stopping for Learning with Noisy Labels.

Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition.

Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Vecnet: A Spectral and Multi-Scale Spatial Fusion Deep Network for Pixel-Level Cloud Type Classification in Himawari-8 Imagery.

Zhaoqing Wang, Xiangyu Kong, Zhanbei Cui, Ming Wu, Chuang Zhang, Mingming Gong, Tongliang Liu

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

Towards Defending against Adversarial Examples via Attack-Invariant Features.

Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao

Proceedings of the 38th International Conference on Machine Learning, 2021

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels.

Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

Proceedings of the 38th International Conference on Machine Learning, 2021

Provably End-to-end Label-noise Learning without Anchor Points.

Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks.

Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Diverse-Structured Networks for Adversarial Robustness.

Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

Confidence Scores Make Instance-dependent Label-noise Learning Possible.

Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

Robust early-learning: Hindering the memorization of noisy labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang

Proceedings of the 9th International Conference on Learning Representations, 2021

Boosting Fairness for Masked Face Recognition.

Jun Yu, Xinlong Hao, Zeyu Cui, Peng He, Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Removing Adversarial Noise in Class Activation Feature Space.

Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data.

Yingbin Bai, Tongliang Liu

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

A Second-Order Approach to Learning With Instance-Dependent Label Noise.

Zhaowei Zhu, Tongliang Liu, Yang Liu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Revisiting Knowledge Distillation: An Inheritance and Exploration Framework.

Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

A Machine Learning Approach for Predicting Human Preference for Graph Layouts^*.

Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu

Proceedings of the 14th IEEE Pacific Visualization Symposium, 2021

Learning with Group Noise.

Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model.

Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Harnessing Side Information for Classification Under Label Noise.

Yang Wei, Chen Gong, Shuo Chen, Tongliang Liu, Jian Yang, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2020

Why ResNet Works? Residuals Generalize.

Fengxiang He, Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2020

Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search.

Cheng Deng, Erkun Yang, Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2020

Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm.

Yihang Lou, Ling-Yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen Gao

IEEE Trans. Multim., 2020

Group Feedback Capsule Network.

Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu

IEEE Trans. Image Process., 2020

Adversarial Examples for Hamming Space Search.

Erkun Yang, Tongliang Liu, Cheng Deng, Dacheng Tao

IEEE Trans. Cybern., 2020

Multiple Kernel $k$k-Means with Incomplete Kernels.

Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Dinggang Shen, Jianping Yin, Wen Gao

IEEE Trans. Pattern Anal. Mach. Intell., 2020

Absent Multiple Kernel Learning Algorithms.

Xinwang Liu, Lei Wang, Xinzhong Zhu, Miaomiao Li, En Zhu, Tongliang Liu, Li Liu, Yong Dou, Jianping Yin

IEEE Trans. Pattern Anal. Mach. Intell., 2020

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19.

Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang

CoRR, 2020

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao

CoRR, 2020

A Survey of Label-noise Representation Learning: Past, Present and Future.

Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama

CoRR, 2020

Maximum Mean Discrepancy is Aware of Adversarial Attacks.

Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

CoRR, 2020

Experimental Quantum Generative Adversarial Networks for Image Generation.

Heliang Huang, Yuxuan Du, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, Rui Yang, Tongliang Liu, Min-Hsiu Hsieh, Hui Deng, Hao Rong, Cheng-Zhi Peng, Chao-Yang Lu, Yu-Ao Chen, Dacheng Tao, Xiaobo Zhu, Jian-Wei Pan

CoRR, 2020

Weakly Supervised Temporal Action Localization with Segment-Level Labels.

Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu

CoRR, 2020

Parts-dependent Label Noise: Towards Instance-dependent Label Noise.

Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama

CoRR, 2020

Class2Simi: A New Perspective on Learning with Label Noise.

Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

CoRR, 2020

Repulsive Mixture Models of Exponential Family PCA for Clustering.

Maoying Qiao, Tongliang Liu, Jun Yu, Wei Bian, Dacheng Tao

CoRR, 2020

Quantum noise protects quantum classifiers against adversaries.

Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao, Nana Liu

CoRR, 2020

Multi-Class Classification from Noisy-Similarity-Labeled Data.

Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

CoRR, 2020

Towards Mixture Proportion Estimation without Irreducibility.

Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao

CoRR, 2020

Domain Generalization via Entropy Regularization.

Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning.

Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Part-dependent Label Noise: Towards Instance-dependent Label Noise.

Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval.

Shikang Gan, Yong Luo, Yonggang Wen, Tongliang Liu, Han Hu

Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks.

Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian

Proceedings of the 37th International Conference on Machine Learning, 2020

Label-Noise Robust Domain Adaptation.

Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao

Proceedings of the 37th International Conference on Machine Learning, 2020

LTF: A Label Transformation Framework for Correcting Label Shift.

Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning with Bounded Instance and Label-dependent Label Noise.

Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao

Proceedings of the 37th International Conference on Machine Learning, 2020

Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces.

Jiankang Deng, Jia Guo, Tongliang Liu, Mingming Gong, Stefanos Zafeiriou

Proceedings of the Computer Vision - ECCV 2020, 2020

Generative-Discriminative Complementary Learning.

Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Diversified Bayesian Nonnegative Matrix Factorization.

Maoying Qiao, Jun Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space.

Xinmei Tian, Ya Li, Tongliang Liu, Xinchao Wang, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2019

Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning.

Chen Gong, Tongliang Liu, Jian Yang, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2019

Adaptive Morphological Reconstruction for Seeded Image Segmentation.

Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongying Meng, Asoke K. Nandi

IEEE Trans. Image Process., 2019

Unsupervised Semantic-Preserving Adversarial Hashing for Image Search.

Cheng Deng, Erkun Yang, Tongliang Liu, Jie Li, Wei Liu, Dacheng Tao

IEEE Trans. Image Process., 2019

Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain.

Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Truncated Cauchy Non-Negative Matrix Factorization.

Naiyang Guan, Tongliang Liu, Yangmuzi Zhang, Dacheng Tao, Larry S. Davis

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Advances in data representation and learning for pattern analysis.

C. L. Philip Chen, Xinge You, Xinbo Gao, Tongliang Liu, Fionn Murtagh, Weifeng Liu

Neurocomputing, 2019

Where is the Bottleneck of Adversarial Learning with Unlabeled Data?

Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama

CoRR, 2019

A Quantum-inspired Algorithm for General Minimum Conical Hull Problems.

Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao

CoRR, 2019

Multi-View Matrix Completion for Multi-Label Image Classification.

Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu

CoRR, 2019

On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning.

Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao

CoRR, 2019

Robust Angular Local Descriptor Learning.

Yanwu Xu, Mingming Gong, Tongliang Liu, Kayhan Batmanghelich, Chaohui Wang

CoRR, 2019

Are Anchor Points Really Indispensable in Label-Noise Learning?

Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence.

Fengxiang He, Tongliang Liu, Dacheng Tao

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer.

Yasutaka Furusho, Tongliang Liu, Kazushi Ikeda

Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Positive and Unlabeled Learning with Label Disambiguation.

Chuang Zhang, Dexin Ren, Tongliang Liu, Jian Yang, Chen Gong

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm.

Yihang Lou, Ling-Yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen Gao

Proceedings of the IEEE International Conference on Multimedia and Expo, 2019

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs.

Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, Dacheng Tao

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Multiclass Learning With Partially Corrupted Labels.

Ruxin Wang, Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2018

Continuous Dropout.

Xu Shen, Xinmei Tian, Tongliang Liu, Fang Xu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2018

On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning.

Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2018

Supervised Discrete Hashing With Relaxation.

Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan

IEEE Trans. Neural Networks Learn. Syst., 2018

Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks.

Kede Ma, Huan Fu, Tongliang Liu, Zhou Wang, Dacheng Tao

IEEE Trans. Image Process., 2018

A Regularization Approach for Instance-Based Superset Label Learning.

Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, Dacheng Tao

IEEE Trans. Cybern., 2018

Fast Supervised Discrete Hashing.

Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan

IEEE Trans. Pattern Anal. Mach. Intell., 2018

An Optimal Transport View on Generalization.

Jingwei Zhang, Tongliang Liu, Dacheng Tao

CoRR, 2018

The Expressive Power of Parameterized Quantum Circuits.

Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao

CoRR, 2018

Implementable Quantum Classifier for Nonlinear Data.

Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao

CoRR, 2018

Instance-Dependent PU Learning by Bayesian Optimal Relabeling.

Fengxiang He, Tongliang Liu, Geoffrey I. Webb, Dacheng Tao

CoRR, 2018

Domain Generalization via Conditional Invariant Representation.

Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao

CoRR, 2018

An Information-Theoretic View for Deep Learning.

Jingwei Zhang, Tongliang Liu, Dacheng Tao

CoRR, 2018

Semantic Structure-based Unsupervised Deep Hashing.

Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, Dacheng Tao

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Online Heterogeneous Transfer Metric Learning.

Yong Luo, Tongliang Liu, Yonggang Wen, Dacheng Tao

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization.

Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Learning with Biased Complementary Labels.

Xiyu Yu, Tongliang Liu, Mingming Gong, Dacheng Tao

Proceedings of the Computer Vision - ECCV 2018, 2018

Correcting the Triplet Selection Bias for Triplet Loss.

Baosheng Yu, Tongliang Liu, Mingming Gong, Changxing Ding, Dacheng Tao

Proceedings of the Computer Vision - ECCV 2018, 2018

Deep Domain Generalization via Conditional Invariant Adversarial Networks.

Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao

Proceedings of the Computer Vision - ECCV 2018, 2018

An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption.

Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, Dacheng Tao

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Robust Angular Local Descriptor Learning.

Yanwu Xu, Mingming Gong, Tongliang Liu, Kayhan Batmanghelich, Chaohui Wang

Proceedings of the Computer Vision - ACCV 2018, 2018

Reliable Multi-View Clustering.

Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, Jubo Zhu

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Domain Generalization via Conditional Invariant Representations.

Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Large-Cone Nonnegative Matrix Factorization.

Tongliang Liu, Mingming Gong, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2017

Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence.

Hongfu Liu, Junjie Wu, Tongliang Liu, Dacheng Tao, Yun Fu

IEEE Trans. Knowl. Data Eng., 2017

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs.

Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, Dacheng Tao

IEEE Trans. Image Process., 2017

Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification.

Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu, Dacheng Tao

IEEE Trans. Image Process., 2017

Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection.

Yuxiang Zhang, Bo Du, Liangpei Zhang, Tongliang Liu

IEEE Trans. Geosci. Remote. Sens., 2017

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

Tongliang Liu, Dacheng Tao, Mingli Song, Stephen J. Maybank

IEEE Trans. Pattern Anal. Mach. Intell., 2017

General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer.

Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Understanding How Feature Structure Transfers in Transfer Learning.

Tongliang Liu, Qiang Yang, Dacheng Tao

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Algorithmic Stability and Hypothesis Complexity.

Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao

Proceedings of the 34th International Conference on Machine Learning, 2017

On Compressing Deep Models by Low Rank and Sparse Decomposition.

Xiyu Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

The complexity of algorithmic hypothesis class

Tongliang Liu

PhD thesis, 2016

On the Performance of Manhattan Nonnegative Matrix Factorization.

Tongliang Liu, Dacheng Tao

IEEE Trans. Neural Networks Learn. Syst., 2016

Video Face Editing Using Temporal-Spatial-Smooth Warping.

Xiaoyan Li, Tongliang Liu, Jiankang Deng, Dacheng Tao

ACM Trans. Intell. Syst. Technol., 2016

Local Rademacher Complexity for Multi-Label Learning.

Chang Xu, Tongliang Liu, Dacheng Tao, Chao Xu

IEEE Trans. Image Process., 2016

Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing.

Hao Xiong, Tongliang Liu, Dacheng Tao, Heng Tao Shen

IEEE Trans. Image Process., 2016

Representative Vector Machines: A Unified Framework for Classical Classifiers.

Jie Gui, Tongliang Liu, Dacheng Tao, Zhenan Sun, Tieniu Tan

IEEE Trans. Cybern., 2016

Classification with Noisy Labels by Importance Reweighting.

Tongliang Liu, Dacheng Tao

IEEE Trans. Pattern Anal. Mach. Intell., 2016

Dimensionality-Dependent Generalization Bounds for <i>k</i>-Dimensional Coding Schemes.

Tongliang Liu, Dacheng Tao, Dong Xu

Neural Comput., 2016

Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks.

Kede Ma, Huan Fu, Tongliang Liu, Zhou Wang, Dacheng Tao

CoRR, 2016

Dimensionality-Dependent Generalization Bounds for $k$-Dimensional Coding Schemes.

Tongliang Liu, Dacheng Tao, Dong Xu

CoRR, 2016

Domain Adaptation with Conditional Transferable Components.

Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf

Proceedings of the 33nd International Conference on Machine Learning, 2016

Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair.

Hao Xiong, Tongliang Liu, Dacheng Tao

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Deformed Graph Laplacian for Semisupervised Learning.

Chen Gong, Tongliang Liu, Dacheng Tao, Keren Fu, Enmei Tu, Jie Yang

IEEE Trans. Neural Networks Learn. Syst., 2015

Multiview Matrix Completion for Multilabel Image Classification.

Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu

IEEE Trans. Image Process., 2015

No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model.

Yanan Lu, Fengying Xie, Tongliang Liu, Zhiguo Jiang, Dacheng Tao

IEEE Signal Process. Lett., 2015

Spectral Ensemble Clustering.

Hongfu Liu, Tongliang Liu, Junjie Wu, Dacheng Tao, Yun Fu

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Multi-Task Model and Feature Joint Learning.

Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014

Decomposition-Based Transfer Distance Metric Learning for Image Classification.

Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu

IEEE Trans. Image Process., 2014

Learning relative features through adaptive pooling for image classification.

Ming Shao, Sheng Li, Tongliang Liu, Dacheng Tao, Thomas S. Huang, Yun Fu

Proceedings of the IEEE International Conference on Multimedia and Expo, 2014