Zhuang Liu's Homepage

Research Scientist at FAIR

Email: liuzhuangthu at gmail.com

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Zhuang Liu

I'm a Research Scientist at FAIR, Meta AI. I received my PhD from UC Berkeley (Dissertation), advised by Prof. Trevor Darrell. I was fortunate to have worked as a visiting researcher or an intern at Cornell University, Intel Labs, Adobe Research and FAIR. I received my Bachelor's degree from Yao Class at Tsinghua University.

My research aims to advance representation learning by developing novel model architectures and learning algorithms, with a focus on efficiency and scalability in specific areas to promote practical progress. I also place an emphasis on exploring simple and baseline methods to gain insights into the workings of deep learning. My work on DenseNet received CVPR Best Paper Award in 2017.

Selected Publications

(* equal contribution)

	Dropout Reduces Underfitting Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell ICML 2023 [Paper] [Code]
	A ConvNet for the 2020s Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie CVPR 2022 [Paper] [Video] [Code]
	Anytime Dense Prediction with Confidence Adaptivity Zhuang Liu, Hung-Ju Wang, Zhiqiu Xu, Trevor Darrell, Evan Shelhamer ICLR 2022 [Paper] [OpenReview] [Code]
	Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell ICLR 2021 [Paper] [OpenReview] [Video] [Code] Spotlight Presentation
	Rethinking the Value of Network Pruning Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell ICLR 2019 [Paper] [OpenReview] [Code] NeurIPS'18 Compact Neural Networks Workshop Best Paper Award
	Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang ICCV 2017 [Paper] [Code1] [Code2] [Code3 (3rd-party)]
	Densely Connected Convolutional Networks Gao Huang, Zhuang Liu**, Laurens van der Maaten, Kilian Weinberger CVPR 2017 [Paper] [Video] [TPAMI version] [Code] CVPR Best Paper Award

	Dropout Reduces Underfitting Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell ICML 2023 [Paper] [Code]
	A ConvNet for the 2020s Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie CVPR 2022 [Paper] [Video] [Code]
	Anytime Dense Prediction with Confidence Adaptivity Zhuang Liu, Hung-Ju Wang, Zhiqiu Xu, Trevor Darrell, Evan Shelhamer ICLR 2022 [Paper] [OpenReview] [Code]
	Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell ICLR 2021 [Paper] [OpenReview] [Video] [Code] Spotlight Presentation
	Rethinking the Value of Network Pruning Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell ICLR 2019 [Paper] [OpenReview] [Code] NeurIPS'18 Compact Neural Networks Workshop Best Paper Award
	Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang ICCV 2017 [Paper] [Code1] [Code2] [Code3 (3rd-party)]
	Densely Connected Convolutional Networks Gao Huang, Zhuang Liu**, Laurens van der Maaten, Kilian Weinberger CVPR 2017 [Paper] [Video] [TPAMI version] [Code] CVPR Best Paper Award

	Dropout Reduces Underfitting Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell arXiv 2023 [Paper] [Code]
	ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie CVPR 2023 [Paper] [Code]
	Exploring Simple and Transferable Recognition-Aware Image Processing Zhuang Liu, Hungju Wang, Tinghui Zhou, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, Trevor Darrell TPAMI 2022 [Paper] [Code]
	A ConvNet for the 2020s Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie CVPR 2022 [Paper] [Video] [Code]
	Anytime Dense Prediction with Confidence Adaptivity Zhuang Liu, Hung-Ju Wang, Zhiqiu Xu, Trevor Darrell, Evan Shelhamer ICLR 2022 [Paper] [Code] [OpenReview]
	Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric Xing CVPR 2022 [Paper]
	Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell AAAI 2022 [Paper] [Code]
	Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning Yinbo Chen, Zhuang Liu, Huijuan Xu, Trevor Darrell, Xiaolong Wang ICCV 2021 [Paper] [Code]
	SensAI: Fast ConvNets Serving on Live Data via Class Parallelism Guanhua Wang, Zhuang Liu, Siyuan Zhuang, Brandon Hsieh, Joseph E. Gonzalez, Trevor Darrell, Ion Stoica Machine Learning and Systems 2021 [Paper] [Code]
	Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell ICLR 2021 [Paper] [Code] [OpenReview] [Video] Spotlight Presentation
	Test-Time Training for Out-of-Distribution Generalization Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt ICML 2020 [Paper] [Code] [Video]
	MSeg: A Composite Dataset for Multi-domain Semantic Segmentation John Lambert, Zhuang Liu**, Ozan Sener, James Hays, Vladlen Koltun CVPR 2020 [Paper] [Code] [Demo]
	Few Sample Knowledge Distillation for Efficient Network Compression Tianhong Li, Jianguo Li, Zhuang Liu, Changshui Zhang CVPR 2020 [Paper] [Code]
	Few-shot Object Detection via Feature Reweighting Bingyi Kang, Zhuang Liu**, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell ICCV 2019 [Paper] [Code]
	Rethinking the Value of Network Pruning Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell ICLR 2019 [Paper] [Code] [OpenReview] NeurIPS'18 Compact Neural Networks Workshop Best Paper Award
	DSOD: Learning Deeply Supervised Object Detectors from Scratch Zhiqiang Shen, Zhuang Liu**, Jianguo Li, Yu-Gang Jiang, Yurong Chen, Xiangyang Xue ICCV 2017 [Paper] [Code] [TPAMI version]
	Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang ICCV 2017 [Paper] [Code1] [Code2]
	Densely Connected Convolutional Networks Gao Huang, Zhuang Liu**, Laurens van der Maaten, Kilian Weinberger CVPR 2017 [Paper] [Code] [Video] [TPAMI version] CVPR Best Paper Award
	Snapshot Ensembles: Train 1, Get M for Free Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John Hopcroft, Kilian Weinberger ICLR 2017 [Paper] [Code]
	Deep Networks with Stochastic Depth Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger ECCV 2016 [Paper] [Code] Spotlight Presentation

Research Scientist at FAIR

Email: liuzhuangthu at gmail.com

Google Scholar / GitHub /
LinkedIn / Twitter

Zhuang Liu

Selected Publications

Dropout Reduces Underfitting

A ConvNet for the 2020s

Anytime Dense Prediction with Confidence Adaptivity

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

Rethinking the Value of Network Pruning

Learning Efficient Convolutional Networks through Network Slimming

Densely Connected Convolutional Networks

Full List of Publications

Dropout Reduces Underfitting

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

Exploring Simple and Transferable Recognition-Aware Image Processing

A ConvNet for the 2020s

Anytime Dense Prediction with Confidence Adaptivity

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

Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning

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

SensAI: Fast ConvNets Serving on Live Data via Class Parallelism

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

Test-Time Training for Out-of-Distribution Generalization

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

Few Sample Knowledge Distillation for Efficient Network Compression

Few-shot Object Detection via Feature Reweighting

Rethinking the Value of Network Pruning

DSOD: Learning Deeply Supervised Object Detectors from Scratch

Learning Efficient Convolutional Networks through Network Slimming

Densely Connected Convolutional Networks

Snapshot Ensembles: Train 1, Get M for Free

Deep Networks with Stochastic Depth

Misc

Research Scientist at FAIR

Email: liuzhuangthu at gmail.com

Google Scholar / GitHub / LinkedIn / Twitter

Zhuang Liu

Selected Publications

Dropout Reduces Underfitting

A ConvNet for the 2020s

Anytime Dense Prediction with Confidence Adaptivity

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

Rethinking the Value of Network Pruning

Learning Efficient Convolutional Networks through Network Slimming

Densely Connected Convolutional Networks

Full List of Publications

Dropout Reduces Underfitting

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

Exploring Simple and Transferable Recognition-Aware Image Processing

A ConvNet for the 2020s

Anytime Dense Prediction with Confidence Adaptivity

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

Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning

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

SensAI: Fast ConvNets Serving on Live Data via Class Parallelism

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

Test-Time Training for Out-of-Distribution Generalization

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

Few Sample Knowledge Distillation for Efficient Network Compression

Few-shot Object Detection via Feature Reweighting

Rethinking the Value of Network Pruning

DSOD: Learning Deeply Supervised Object Detectors from Scratch

Learning Efficient Convolutional Networks through Network Slimming

Densely Connected Convolutional Networks

Snapshot Ensembles: Train 1, Get M for Free

Deep Networks with Stochastic Depth

Misc

Google Scholar / GitHub /
LinkedIn / Twitter