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Official implementation of the paper: "REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions", IEEE WACV, 2022

License: MIT License

Python 1.30% Jupyter Notebook 98.70%

regroup's Introduction

This repository contains the demo code of the method called REGroup proposed in the paper: REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions, IEEE/CVF WACV, 2022.

Requirements

Pytorch
numpy, scipy
matplotlib
Jupyter notebook
foolbox (version 2.3.0)

Steps to run the demo

Clone the repository.
Download CIFAR10 PGD L-infinity adversarial examples
Open jupyter notebook REGroup_demo_cifar10_vgg19.ipynb

$ git clone https://github.com/lokender/REGroup.git
$ cd REGroup
$ wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1ylJctBJzh4ih-0zzD4ZLO2umh--QpX7u' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1ylJctBJzh4ih-0zzD4ZLO2umh--QpX7u" -O cifar10_vgg19_pgd_examples.mat && rm -rf /tmp/cookies.txt

To-dos?

[Done] Classifier: VGG19, Dataset: CIFAR10 ( Released )
[To-do] Classifier: VGG19, Dataset: ImageNet ( Will be released soon )
[To-do] Classifier: ResNet, Dataset: CIFAR10 ( Will be released soon )
[To-do] Classifier: ResNet, Dataset: ImageNet ( Will be released soon )
[To-do] Classifier: Inception, Dataset: ImageNet ( Will be released soon )
[To-do] Code for building generative classifiers. ( Will be released soon )