Awesome
MixupE
Codes for "MixupE: Understanding and Improving Mixup from Directional Derivative Perspective"
UAI 2023 Best Student Paper
Dataset
For CIFAR-10/100 and SVHN, these datasets can be downloaded automatically with torchvision.
Script for downloading Tiny-Imgane-Net.
Environments
Environment details used for our study.
Python: 3.8.5
PyTorch: 1.11.0+cu10.2
Torchvision: 0.12.0+cu10.2
CUDA: 10.2
NumPy: 1.22.3
scipy 1.5.2
Train with MixupE
python main.py --dataset <Dataset_Name> --data_dir <Dataset_Path> --root_dir <Exp_Path> --train mixupe --mixupe_version 3
Take the CIFAR-100 for example,
python main.py --dataset cifar100 --data_dir ~/data/CIFAR100/ \
--root_dir ./experiments/ --labels_per_class 5000 \
--arch preactresnet50 --epochs 800 --schedule 10 300 600 --gammas 0.01 0.1 0.1 \
--train mixupe --adv_gen none --adv_train none --mixup_alpha 2.0 \
--decay 0.0001 --mixupe_version 3 --mixup_eta 0.001 --learning_rate 0.1 --threshold 1.0
where the hyper-parameters in "--gammas" are "initial learning rate", "learning rate decay factors".
<p align="center"> <img src="./figs/loss_acc.png" width="90%" /> </p>Citation
@inproceedings{zou2023mixupe,
title={Mixupe: Understanding and improving mixup from directional derivative perspective},
author={Zou, Yingtian and Verma, Vikas and Mittal, Sarthak and Tang, Wai Hoh and Pham, Hieu and Kannala, Juho and Bengio, Yoshua and Solin, Arno and Kawaguchi, Kenji},
booktitle={Uncertainty in Artificial Intelligence},
pages={2597--2607},
year={2023},
organization={PMLR}
}