Zhuang Liu

Orcid: 0000-0001-6395-0212

Affiliations:

University of California at Berkeley, CA, USA
Tsinghua University, Beijing, China (former)

According to our database¹, Zhuang Liu authored at least 39 papers between 2016 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

A Decade's Battle on Dataset Bias: Are We There Yet?

[BibT_eX]

[DOI]

Zhuang Liu

Kaiming He

CoRR, 2024

Massive Activations in Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

Deconstructing Denoising Diffusion Models for Self-Supervised Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Exploring Simple and Transferable Recognition-Aware Image Processing.

[BibT_eX]

[DOI]

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

MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation.

[BibT_eX]

[DOI]

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

Initializing Models with Larger Ones.

[BibT_eX]

[DOI]

CoRR, 2023

ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy.

[BibT_eX]

[DOI]

Kirill Vishniakov

Zhiqiang Shen

Zhuang Liu

CoRR, 2023

A Coefficient Makes SVRG Effective.

[BibT_eX]

[DOI]

CoRR, 2023

A Simple and Effective Pruning Approach for Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

[BibT_eX]

[DOI]

CoRR, 2023

Dropout Reduces Underfitting.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning.

[BibT_eX]

[DOI]

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

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.

[BibT_eX]

[DOI]

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

2022

Efficient and Scalable Neural Architectures for Visual Recognition

[BibT_eX]

[DOI]

Zhuang Liu

PhD thesis, 2022

Convolutional Networks with Dense Connectivity.

[BibT_eX]

[DOI]

Gao Huang

Zhuang Liu

Geoff Pleiss

Laurens van der Maaten

Kilian Q. Weinberger

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

Anytime Dense Prediction with Confidence Adaptivity.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space.

[BibT_eX]

[DOI]

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

A ConvNet for the 2020s.

[BibT_eX]

[DOI]

Zhuang Liu

Hanzi Mao

Chao-Yuan Wu

Christoph Feichtenhofer

Trevor Darrell

Saining Xie

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

Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Confidence Adaptive Anytime Pixel-Level Recognition.

[BibT_eX]

[DOI]

Zhuang Liu

Trevor Darrell

Evan Shelhamer

CoRR, 2021

sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data.

[BibT_eX]

[DOI]

Proceedings of Machine Learning and Systems 2021, 2021

Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control.

[BibT_eX]

[DOI]

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

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning.

[BibT_eX]

[DOI]

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

2020

Object Detection from Scratch with Deep Supervision.

[BibT_eX]

[DOI]

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

Rethinking Image Mixture for Unsupervised Visual Representation Learning.

[BibT_eX]

[DOI]

CoRR, 2020

A New Meta-Baseline for Few-Shot Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Few Sample Knowledge Distillation for Efficient Network Compression.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Regularization Matters in Policy Optimization.

[BibT_eX]

[DOI]

CoRR, 2019

Transferable Recognition-Aware Image Processing.

[BibT_eX]

[DOI]

CoRR, 2019

Rethinking the Value of Network Pruning.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

Few-Shot Object Detection via Feature Reweighting.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Knowledge Distillation from Few Samples.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Snapshot Ensembles: Train 1, Get M for Free.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

DSOD: Learning Deeply Supervised Object Detectors from Scratch.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Computer Vision, 2017

Learning Efficient Convolutional Networks through Network Slimming.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Computer Vision, 2017

Densely Connected Convolutional Networks.

[BibT_eX]

[DOI]

Gao Huang

Zhuang Liu

Laurens van der Maaten

Kilian Q. Weinberger

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

2016

Densely Connected Convolutional Networks.

[BibT_eX]

[DOI]

Gao Huang

Zhuang Liu

Kilian Q. Weinberger

CoRR, 2016

Deep Networks with Stochastic Depth.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2016, 2016

Zhuang Liu

Timeline

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