MetAug

This paper is accepted by ICML2022.

Install the PyTorch based environment for MetAug.

# Create a conda environment
conda create -n metaug python=3.7

# Activate the environment
conda activate metaug

# Install dependencies
pip install -r requirements.txt

Install the datasets.

The used datasets are totally established, please follow the official instruction to install the datasets. Note that our code requires the datasets containing the "images" not "features".

How to Run

We provide the running scripts as follows. Make sure you change the paths in data_folder, model_path, and tb_path and run the commands.

# Train
python train_MetAug.py

# Test
python LinearProbing_MetAug.py

Readers can change hyperparameters directly in code or bash script

Checkpoint

We provide the checkpoint in https://drive.google.com/file/d/1uClfQ3u_3U3Kag-a0SSs2LRRI3qW4OA6/view?usp=sharing

Citation

If you find this repo useful for your research, please consider citing the paper

@inproceedings{DBLP:conf/icml/LiQZ0X22,
  author    = {Jiangmeng Li and
               Wenwen Qiang and
               Changwen Zheng and
               Bing Su and
               Hui Xiong},
  editor    = {Kamalika Chaudhuri and
               Stefanie Jegelka and
               Le Song and
               Csaba Szepesv{\'{a}}ri and
               Gang Niu and
               Sivan Sabato},
  title     = {MetAug: Contrastive Learning via Meta Feature Augmentation},
  booktitle = {International Conference on Machine Learning, {ICML} 2022, 17-23 July
               2022, Baltimore, Maryland, {USA}},
  series    = {Proceedings of Machine Learning Research},
  volume    = {162},
  pages     = {12964--12978},
  publisher = {{PMLR}},
  year      = {2022},
  url       = {https://proceedings.mlr.press/v162/li22r.html},
  timestamp = {Tue, 12 Jul 2022 17:36:52 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/LiQZ0X22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

or

@article{DBLP:journals/corr/abs-2203-05119,
  author    = {Jiangmeng Li and
               Wenwen Qiang and
               Changwen Zheng and
               Bing Su and
               Hui Xiong},
  title     = {MetAug: Contrastive Learning via Meta Feature Augmentation},
  journal   = {CoRR},
  volume    = {abs/2203.05119},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2203.05119},
  doi       = {10.48550/arXiv.2203.05119},
  eprinttype = {arXiv},
  eprint    = {2203.05119},
  timestamp = {Wed, 16 Mar 2022 16:41:29 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2203-05119.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}