https://github.com/vita-group/triple-wins

[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“
https://github.com/vita-group/triple-wins

adversarial-attacks adversarial-robustness efficiency efficient-inference robustness triple-wins

Last synced: about 1 month ago
JSON representation

[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“

• Host: GitHub
• URL: https://github.com/vita-group/triple-wins
• Owner: VITA-Group
• Created: 2020-02-13T03:21:43.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-12-30T02:04:04.000Z (over 3 years ago)
• Last Synced: 2025-03-29T09:42:11.461Z (about 2 months ago)
• Topics: adversarial-attacks, adversarial-robustness, efficiency, efficient-inference, robustness, triple-wins
• Language: Python
• Homepage:
• Size: 13.2 MB
• Stars: 24
• Watchers: 12
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Triple-Wins

[ICLR 20] Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference

## Prerequisite 

This code requires `Pytorch 1.1.0`

### BibTeX

    @inproceedings{tkhu2019triplewins,

    author={Ting{-}Kuei Hu and Tianlong Chen and Haotao Wang and Zhangyang Wang},

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

    booktitle={ICLR},

    year = {2020},

    }

### STATUS

upload mnist code -- 2020/03/17

### TODOS

upload mnist pretrained model ( Done on 10/01/2021)

upload cifar code ( Done on 10/01/2021)

upload cifar pretrained model ( Done on 10/01/2021)