Link to the paper: https://arxiv.org/pdf/2111.14271.pdf
- Zhibo Zhang (zhibozhang@cs.toronto.edu< 7CD5 /a>)
- Jongseong Jang (j.jang@lgresearch.ai)
- Ruiwen Li (ruiwen.li@mail.utoronto.ca)
Copyright (c) 2021 LG AI Research and University of Toronto, all rights reserved.
If you use our code, please cite our paper:
@misc{zhang2021excon,
title={ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification},
author={Zhibo Zhang and Jongseong Jang and Chiheb Trabelsi and Ruiwen Li and Scott Sanner and Yeonjeong Jeong and Dongsub Shim},
year={2021},
eprint={2111.14271},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Prepare the Tiny ImageNet dataset (in the path where you want to save the dataset):
wget -nc https://image-net.org/data/tiny-imagenet-200.zip
unzip tiny-imagenet-200.zip
python3 ExCon/utils/val_format.py
CIFAR-100 dataset
python3 ExCon/main_supcon.py --epochs=200 --explainer="GradCAM" --dataset="cifar100" --batch_size=256 --method="Ex_SupCon" --learning_rate=0.5 --temp=0.1 --cosine --negative_pair=1 --validation=0 --background_anchor=0 --exp_epochs=50Tiny ImageNet dataset
python3 ExCon/main_supcon.py --epochs=200 --explainer="GradCAM" --dataset="ImageNet" --batch_size=128 --method="Ex_SupCon" --learning_rate=0.5 --temp=0.1 --cosine --negative_pair=1 --validation=0 --background_anchor=0 --exp_epochs=0 --data_folder=$PATH_TO_DATASET