Code for Max-Margin Contrastive Learning - AAAI 2022

Related tags

Deep LearningMMCL
Overview

Max-Margin Contrastive Learning

This is a pytorch implementation for the paper Max-Margin Contrastive Learning accepted to AAAI 2022.

This repository is based on SimCLR-pytorch.

For CIFAR100 scripts, please check CIFAR100/.

[arXiv] [Video]

Set-up environment

  • conda env create -f mmcl_env.yaml
  • conda activate mmcl

Prepare data

  • export IMAGENET_PATH=path_to_dataset
  • Find ImageNet-100 classes here

Train

  • Train MMCL models using one of the following commands:
  • ImageNet-1k:
    • python train.py --config configs/imagenet1k_mmcl_pgd.yaml
    • python train.py --config configs/imagenet1k_mmcl_inv.yaml
  • ImageNet-100:
    • python train.py --config configs/imagenet100_mmcl_pgd.yaml
    • python train.py --config configs/imagenet100_mmcl_inv.yaml

Linear-Evaluation

  • ImageNet-1k models:
    • python train.py --config configs/imagenet_eval.yaml --encoder_ckpt path_to_experiment_folder//checkpoint-500400.pth.tar
  • ImageNet-100 models:
    • python train.py --config configs/imagenet100_eval.yaml --encoder_ckpt path_to_experiment_folder/checkpoint-198000.pth.tar
  • Experiment folder can be found in logs/exman-train.py/runs/X
  • Following are some results and pretrained models.
Model Linear-Evaluation Pretrained Model
ImageNet-1K MMCL PGD 63.7 here
ImageNet-100 MMCL PGD 80.7 here

Citation

If you find this repository useful in your research, please cite:

@misc{shah2021maxmargin,
      title={Max-Margin Contrastive Learning}, 
      author={Anshul Shah and Suvrit Sra and Rama Chellappa and Anoop Cherian},
      year={2021},
      eprint={2112.11450},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Owner
Anshul Shah
Anshul Shah
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