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MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

MetaMask contains the official implementation of the NIPS 2022 paper: MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning. The code is based on the Barlowtwins implementation.

We provide the result and the pre-trained model for SimCLR+MetaMask method based on the Cifar10 dataset.

Installation

Requirements

• Linux with Python3.7
• PyTorch == 1.10.1
• torchvision that matches the PyTorch installation
• lightly == 1.0.8
• pytorch-lightning==1.5.10
• setuptools == 52.0.0
• CUDA 11.1

Build MetaMask

• Create a virtual environment.

conda create -n metamask python=3.7
conda activate metamask

• Install PyTorch. We use pytorch1.10.1 in our experiments. To install pytorch-1.10.1:

pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/torch_stable.html

• Install other requirements.

pip install lightly==1.0.8
pip install setuptools==52.0.0
pip install pytorch-lightning==1.5.10

Code Structure

• data: Dataset files (Files for Cifar10 will be downloaded automatically).
• models
  • mask_generator.py: Code for generating masks.
  • simsiam.py: Code for feature extraction.
• loss.py: Code for BarlowTwinsLoss.
• meta-mask.py: Code for the training and testing pipeline.
• utils.py: Code for KNN prediction.

Models

The classification accuracy (top 1) of SimCLR+MetaMask is 86.01. We provide the model for download.

Getting Started

Training in Command Line

To train a model, run

python metamask.py

The parameters that can be modified are detailed in metamask.py.

Evaluation in Command Line

To evaluate the trained models, run

python metamask.py --ckpt-path "path to the checkpoint file" --eval-only

Citation

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

@article{metamask2022jml,
  author    = {Jiangmeng Li and
               Wenwen Qiang and
               Yanan Zhang and
               Wenyi Mo and
               Changwen Zheng and
               Bing Su and
               Hui Xiong},
  title     = {MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning},
  journal   = {CoRR}
  year      = {2022},
  eprinttype = {arXiv}
}