└── README.md


/README.md:
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 1 | # RobustMAE (ICCV 2023)
 2 | This repository provides the official PyTorch implementation of the following conference paper: 
 3 | > [**Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting (ICCV 2023)**](https://arxiv.org/abs/2308.10315) <br>
 4 | > [Qidong Huang](https://shikiw.github.io/)<sup>1</sup>, 
 5 | > [Xiaoyi Dong](https://scholar.google.com/citations?user=FscToE0AAAAJ&hl=en)<sup>2</sup>, 
 6 | > [Dongdong Chen](https://www.dongdongchen.bid/)<sup>3</sup>,
 7 | > [Yinpeng Chen](https://scholar.google.com/citations?user=V_VpLksAAAAJ&hl=en)<sup>3</sup>,
 8 | > [Lu Yuan](https://scholar.google.com/citations?user=k9TsUVsAAAAJ&hl=zh-CN)<sup>3</sup>, 
 9 | > [Gang Hua](https://www.ganghua.org/)<sup>4</sup>, 
10 | > [Weiming Zhang](http://staff.ustc.edu.cn/~zhangwm/index.html)<sup>1</sup>, 
11 | > [Nenghai Yu](https://scholar.google.com/citations?user=7620QAMAAAAJ&hl=en)<sup>1</sup> <br>
12 | > <sup>1</sup>University of Science and Technology of China, <sup>2</sup>Shanghai AI Lab, <sup>3</sup>Microsoft, <sup>4</sup>Wormpex AI Research <br>
13 | >
14 | 
15 | 
16 | ## To Do
17 | - [ ] Release training code
18 | - [ ] Release evalaution code
19 | 
20 | 


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