Awesome
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
This is the official project repository for DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization [ICCV 2021].
Zirui Liu*, Haifeng Jin*, Ting-Hsiang Wang, Kaixiong Zhou, and Xia Hu.
TL; DR. DivAug is a unsupervised automated data augmentation method without requiring a separate search process.
Implementation
The ./cpp_extension directory contains the C++ implementation of the parallel KMEAN++ algorithm based on OpenMP.
Our implementation of KMEAN++ is based on KMEAN module in scikit-learn.
We also provide the vanilla python implementation of KMEAN++ in ./common/utils:line 48.
Install
- Requirements
torch>=1.8.0
torchvision>=0.9.0
warmup_scheduler (https://github.com/ildoonet/pytorch-gradual-warmup-lr)
- Build
cd cpp_extension
pip install -v -e .
Reproduce results
See ./scripts. For example, if you want to train wide-resnet-28-10 with DivAug on CIFAR-100, just run
bash scripts/wresnet_28x10divaug_cifar100.sh
Citation
@misc{liu2021divaug,
title={DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization},
author={Zirui Liu and Haifeng Jin and Ting-Hsiang Wang and Kaixiong Zhou and Xia Hu},
year={2021},
eprint={2103.14545},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgement
Our codebase is mainly based on
https://github.com/kakaobrain/fast-autoaugment