Quasi-regular Facade Structure Extraction. - Tian Han 001; Chun Liu; Chiew-Lan Tai; Long Quan (ACCV (4), 2012)

Parsing façade with rank-one approximation. - Chao Yang; Tian Han 001; Long Quan; Chiew-Lan Tai (CVPR, 2012)

Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation. - Tian Han 001; Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (CoRR, 2016)

Alternating Back-Propagation for Generator Network. - Tian Han 001; Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (AAAI, 2017)

Learning Multi-view Generator Network for Shared Representation. - Tian Han 001; Xianglei Xing; Ying Nian Wu (ICPR, 2018)

Replicating Active Appearance Model by Generator Network. - Tian Han 001; Jiawen Wu; Ying Nian Wu (IJCAI, 2018)

Survey on person re-identification based on deep learning. - Kejun Wang; Haolin Wang; Meichen Liu; Xianglei Xing; Tian Han 001 (CAAI Trans. Intell. Technol., 2018)

Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. - Xianglei Xing; Ruiqi Gao; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. - Ying Nian Wu; Ruiqi Gao; Tian Han 001; Song-Chun Zhu (CoRR, 2018)

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. - Tian Han 001; Erik Nijkamp; Xiaolin Fang 002; Mitch Hill; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. - Tian Han 001; Erik Nijkamp; Xiaolin Fang 002; Mitch Hill; Song-Chun Zhu; Ying Nian Wu (CVPR, 2019)

Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. - Xianglei Xing; Tian Han 001; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (CVPR, 2019)

Learning Generator Networks for Dynamic Patterns. - Tian Han 001; Yang Lu 006; Jiawen Wu; Xianglei Xing; Ying Nian Wu (WACV, 2019)

360 Degree Panorama Synthesis From Sequential Views Based on Improved FC-Densenets. - Dandan Zhu 001; Qiangqiang Zhou; Tian Han 001; Yongqing Chen (IEEE Access, 2019)

Deep Unsupervised Clustering with Clustered Generator Model. - Dandan Zhu 001; Tian Han 001; Linqi Zhou; Xiaokang Yang 001; Ying Nian Wu (CoRR, 2019)

Learning Deep Generative Models with Short Run Inference Dynamics. - Erik Nijkamp; Bo Pang 004; Tian Han 001; Linqi Zhou; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model. - Tian Han 001; Xianglei Xing; Jiawen Wu; Ying Nian Wu (Neural Comput., 2019)

Unsupervised Learning and Understanding of Deep Generative Models. - Tian Han 001 (2019)

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. - Erik Nijkamp; Mitch Hill; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (AAAI, 2020)

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. - Tian Han 001; Erik Nijkamp; Linqi Zhou; Bo Pang 004; Song-Chun Zhu; Ying Nian Wu (CVPR, 2020)

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. - Erik Nijkamp; Bo Pang 004; Tian Han 001; Linqi Zhou; Song-Chun Zhu; Ying Nian Wu (ECCV (6), 2020)

Ransp: Ranking Attention Network For Saliency Prediction On Omnidirectional Images. - Dandan Zhu 001; Yongqing Chen; Tian Han 001; Defang Zhao; Yucheng Zhu; Qiangqiang Zhou; Guangtao Zhai; Xiaokang Yang 001 (ICME, 2020)

Saliency Prediction on Omnidirectional Images with Brain-Like Shallow Neural Network. - Dandan Zhu 001; Yongqing Chen; Xiongkuo Min; Defang Zhao; Yucheng Zhu; Qiangqiang Zhou; Xiaokang Yang 001; Tian Han 001 (ICPR, 2020)

Learning Latent Space Energy-Based Prior Model. - Bo Pang 004; Tian Han 001; Erik Nijkamp; Song-Chun Zhu; Ying Nian Wu (NeurIPS, 2020)

Learning Latent Space Energy-Based Prior Model for Molecule Generation. - Bo Pang 004; Tian Han 001; Ying Nian Wu (CoRR, 2020)

Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling. - Bo Pang 004; Erik Nijkamp; Jiali Cui; Tian Han 001; Ying Nian Wu (CoRR, 2020)

Generative Text Modeling through Short Run Inference. - Bo Pang 004; Erik Nijkamp; Tian Han 001; Ying Nian Wu (EACL, 2021)

Lavs: A Lightweight Audio-Visual Saliency Prediction Model. - Dandan Zhu 001; Defang Zhao; Xiongkuo Min; Tian Han 001; Qiangqiang Zhou; Shaobo Yu; Yongqing Chen; Guangtao Zhai; Xiaokang Yang 001 (ICME, 2021)

STAR: Sparse Transformer-based Action Recognition. - Feng Shi 006; Chonghan Lee; Liang Qiu 001; Yizhou Zhao; Tianyi Shen; Shivran Muralidhar; Tian Han 001; Song-Chun Zhu; Vijaykrishnan Narayanan (CoRR, 2021)

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. - Quanshi Zhang; Tian Han 001; Lixin Fan; Zhanxing Zhu; Hang Su; Ying Nian Wu; Jie Ren 018; Hao Zhang 063 (CoRR, 2021)

GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis. - Yizhou Zhao; Wensi Ai; Liang Qiu 001; Pan Lu; Feng Shi 006; Tian Han 001; Song-Chun Zhu (CoRR, 2021)

Towards multi-scale deep features learning with correlation metric for person re-identification. - Dandan Zhu 001; Qiangqiang Zhou; Tian Han 001; Yongqing Chen; Defang Zhao; Xiaokang Yang 001 (Knowl. Based Syst., 2021)

Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs. - Chang Lu; Tian Han 001; Yue Ning 001 (AAAI, 2022)

Learning from the Tangram to Solve Mini Visual Tasks. - Yizhou Zhao; Liang Qiu 001; Pan Lu; Feng Shi 006; Tian Han 001; Song-Chun Zhu (AAAI, 2022)

Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model. - Zhisheng Xiao; Tian Han 001 (NeurIPS, 2022)

Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. - Xianglei Xing; Ruiqi Gao; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. - Jiali Cui; Ying Nian Wu; Tian Han 001 (CVPR, 2023)

Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. - Jiali Cui; Ying Nian Wu; Tian Han 001 (ICCV, 2023)

Learning Energy-based Model via Dual-MCMC Teaching. - Jiali Cui; Tian Han 001 (NeurIPS, 2023)

Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. - Deqian Kong; Bo Pang 004; Tian Han 001; Ying Nian Wu (UAI, 2023)

Learning Multimodal Latent Generative Models with Energy-Based Prior. - Shiyu Yuan; Jiali Cui; Hanao Li; Tian Han 001 (ECCV (89), 2024)

Layerwise Change of Knowledge in Neural Networks. - Xu Cheng 005; Lei Cheng 006; Zhaoran Peng; Yang Xu; Tian Han 001; Quanshi Zhang (ICML, 2024)

Learning Latent Space Hierarchical EBM Diffusion Models. - Jiali Cui; Tian Han 001 (ICML, 2024)

Improving Adversarial Energy-Based Model via Diffusion Process. - Cong Geng; Tian Han 001; Peng-Tao Jiang; Hao Zhang 063; Jinwei Chen; Søren Hauberg; Bo Li 115 (ICML, 2024)