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@inproceedings{zhou2022adversarial,
title={Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks},
author={Jianan Zhou and Jianing Zhu and Jingfeng Zhang and Tongliang Liu and Gang Niu and Bo Han and Masashi Sugiyama},
booktitle={Advances in Neural Information Processing Systems},
year={2022},
url={https://openreview.net/forum?id=s7SukMH7ie9}
}

# Two-stage baseline for MNIST/Kuzushiji
python main.py --dataset 'kuzushiji' --model 'cnn' --method 'log' --framework 'two_stage' --cl_epochs 50 --adv_epochs 50 --cl_lr 0.001 --at_lr 0.01
# Two-stage baseline for CIFAR10/SVHN
python main.py --dataset 'cifar10' --model 'resnet18' --method 'log' --framework 'two_stage' --cl_epochs 50 --adv_epochs 70 --cl_lr 0.01 --at_lr 0.01

# Complementary baselines (e.g., LOG) for MNIST/Kuzushiji
python main.py --dataset 'kuzushiji' --model 'cnn' --method 'log' --framework 'one_stage' --adv_epochs 100 --at_lr 0.01 --scheduler 'none'
# Complementary baselines (e.g., LOG) for CIFAR10/SVHN
python main.py --dataset 'cifar10' --model 'resnet18' --method 'log' --framework 'one_stage' --adv_epochs 120 --at_lr 0.01 --scheduler 'none'

# MNIST/Kuzushiji
python main.py --dataset 'kuzushiji' --model 'cnn' --method 'log_ce' --framework 'one_stage' --adv_epochs 100 --at_lr 0.01 --scheduler 'cosine' --sch_epoch 50 --warmup_epoch 10
# CIFAR10/SVHN
python main.py --dataset 'cifar10' --model 'resnet18' --method 'log_ce' --framework 'one_stage' --adv_epochs 120 --at_lr 0.01 --scheduler 'cosine' --sch_epoch 40 --warmup_epoch 40

# Supported Datasets (we cannot handle cifar100 on the SCL setting currently, i.e., complementary learning fails on CIFAR100 in our exp.)
--dataset - ['mnist', 'kuzushiji', 'fashion', 'cifar10', 'svhn', 'cifar100']
# Complementary Loss Functions
--method - ['free', 'nn', 'ga', 'pc', 'forward', 'scl_exp', 'scl_nl', 'mae', 'mse', 'ce', 'gce', 'phuber_ce', 'log', 'exp', 'l_uw', 'l_w', 'log_ce', 'exp_ce']
# Multiple Complementary Labels (MCLs)
--cl_num - (1-9) the number of complementary labels of each data; (0) MCLs data distribution of ICML2020 - "Learning with Multiple Complementary Labels"