no code implementations • NeurIPS 2021 • Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
no code implementations • 29 Sep 2021 • Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang
In addition, role diversity can help to find a better training strategy and increase performance in cooperative MARL.
no code implementations • ICCV 2021 • Hang Xu, Ning Kang, Gengwei Zhang, Chuanlong Xie, Xiaodan Liang, Zhenguo Li
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
no code implementations • NeurIPS 2021 • Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, LiWei Wang
We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.
no code implementations • 21 Jan 2021 • Haotian Ye, Chuanlong Xie, Yue Liu, Zhenguo Li
One of the definitions of OOD accuracy is worst-domain accuracy.
no code implementations • 22 Dec 2020 • Fengwei Zhou, Jiawei Li, Chuanlong Xie, Fei Chen, Lanqing Hong, Rui Sun, Zhenguo Li
Automated data augmentation has shown superior performance in image recognition.
no code implementations • 14 Aug 2020 • Zeng Li, Chuanlong Xie, Qinwen Wang
Furthermore, the finite-sample distribution and the confidence interval of the prediction risk are provided.
no code implementations • 13 Jun 2020 • Chuanlong Xie, Haotian Ye, Fei Chen, Yue Liu, Rui Sun, Zhenguo Li
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.