1 code implementation • 4 Sep 2023 • Shuyang Yu, Junyuan Hong, Haobo Zhang, Haotao Wang, Zhangyang Wang, Jiayu Zhou
Training a high-performance deep neural network requires large amounts of data and computational resources.
1 code implementation • ICLR 2023 • Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL.
1 code implementation • 12 Oct 2022 • Haotao Wang, Junyuan Hong, Aston Zhang, Jiayu Zhou, Zhangyang Wang
As a result, both the stem and the classification head in the final network are hardly affected by backdoor training samples.
no code implementations • 4 Jul 2022 • Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang
Increasing concerns have been raised on deep learning fairness in recent years.
1 code implementation • 4 Jul 2022 • Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, Zhangyang Wang
However, in real-world applications, it is common for the training sets to have long-tailed distributions.
1 code implementation • 4 Jul 2022 • Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang
In addition, NoFrost achieves a $23. 56\%$ adversarial robustness against PGD attack, which improves the $13. 57\%$ robustness in BN-based AT.
1 code implementation • ICLR 2022 • Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
1 code implementation • NeurIPS 2021 • Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang
Diversity and hardness are two complementary dimensions of data augmentation to achieve robustness.
no code implementations • 29 Sep 2021 • Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang
In this paper, we first propose a new fairness goal, termed Equalized Robustness (ER), to impose fair model robustness against unseen distribution shifts across majority and minority groups.
1 code implementation • 18 Jun 2021 • Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
In this paper, we study a novel FL strategy: propagating adversarial robustness from rich-resource users that can afford AT, to those with poor resources that cannot afford it, during federated learning.
no code implementations • 9 Jun 2021 • Sina Mohseni, Haotao Wang, Zhiding Yu, Chaowei Xiao, Zhangyang Wang, Jay Yadawa
The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations.
no code implementations • CVPR 2021 • Zhihua Wang, Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.
no code implementations • 1 Jan 2021 • Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma
Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis.
no code implementations • ICLR 2021 • Jiayi Shen, Haotao Wang, Shupeng Gui, Jianchao Tan, Zhangyang Wang, Ji Liu
The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios.
1 code implementation • NeurIPS 2020 • Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, Zhangyang Wang
The trained model could be adjusted among different standard and robust accuracies "for free" at testing time.
2 code implementations • ECCV 2020 • Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, Zhangyang Wang
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices.
3 code implementations • ICML 2020 • Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Celine Lin, Zhangyang Wang
Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.
2 code implementations • ICLR 2020 • Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma
On the other hand, the trained classifiers have traditionally been evaluated on small and fixed sets of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.
2 code implementations • ICLR 2020 • Ting-Kuei Hu, Tianlong Chen, Haotao Wang, Zhangyang Wang
Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).
5 code implementations • 12 Jun 2019 • Zhen-Yu Wu, Haotao Wang, Zhaowen Wang, Hailin Jin, Zhangyang Wang
We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem.
2 code implementations • NeurIPS 2019 • Shupeng Gui, Haotao Wang, Chen Yu, Haichuan Yang, Zhangyang Wang, Ji Liu
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.