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Haotao Wang

Texas A&M University

AugMax: Adversarial Composition of Random Augmentations for Robust Training

Oct 26, 2021
Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang

* NeurIPS, 2021 

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Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning

Jun 18, 2021
Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou

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Practical Machine Learning Safety: A Survey and Primer

Jun 09, 2021
Sina Mohseni, Haotao Wang, Zhiding Yu, Chaowei Xiao, Zhangyang Wang, Jay Yadawa

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Troubleshooting Blind Image Quality Models in the Wild

May 14, 2021
Zhihua Wang, Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

* 7 pages, 3 tables 

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Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free

Nov 10, 2020
Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, Zhangyang Wang

* NeurIPS 2020 

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GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework

Aug 25, 2020
Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, Zhangyang Wang

* ECCV 2020 spotlight 

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AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

Jul 06, 2020
Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang

* Accepted at ICML2020 

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I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively

Feb 25, 2020
Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

* Accepted to ICLR 2020 

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Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference

Feb 25, 2020
Ting-Kuei Hu, Tianlong Chen, Haotao Wang, Zhangyang Wang

* Published on ICLR 2020 

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Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset

Jul 29, 2019
Haotao Wang, Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, Hailin Jin

* Submitted to TPAMI, under review. arXiv admin note: text overlap with arXiv:1807.08379 

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Adversarially Trained Model Compression: When Robustness Meets Efficiency

Feb 10, 2019
Shupeng Gui, Haotao Wang, Chen Yu, Haichuan Yang, Zhangyang Wang, Ji Liu

* 29 pages, 15 figures, 11 tables 

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