Mutual Contrastive Learning for Visual Representation Learning

This project provides source code for our Mutual Contrastive Learning for Visual Representation Learning (MCL).

Installation

Requirements

Ubuntu 18.04 LTS

Python 3.8 (Anaconda is recommended)

CUDA 11.1

PyTorch 1.7.0

NCCL for CUDA 11.1

Supervised Learning on CIFAR-100 dataset

Dataset

CIFAR-100 : download

unzip to the ./data folder

Training two baseline networks

python main_cifar.py --arch resnet32 --number-net 2

More commands for training various architectures can be found in scripts/train_cifar_baseline.sh

Training two networks by MCL

python main_cifar.py --arch resnet32  --number-net 2 \
    --alpha 0.1 --gamma 1. --beta 0.1 --lam 1. 

More commands for training various architectures can be found in scripts/train_cifar_mcl.sh

Results of MCL on CIFAR-100

We perform all experiments on a single NVIDIA RTX 3090 GPU (24GB) with three runs.

Training multiple networks by MCL combined with Logit distillation

python main_cifar.py --arch WRN_16_2  --number-net 4 \
    --alpha 0.1 --gamma 1. --beta 0.1 --lam 1. \
    --logit-distill

More commands for training various architectures can be found in scripts/train_cifar_mcl_logit.sh

Results of MCL combined with logit distillation on CIFAR-100

We perform all experiments on a single NVIDIA RTX 3090 GPU (24GB) with three runs.

Supervised Learning on ImageNet dataset

Dataset preparation

• Download the ImageNet dataset to YOUR_IMAGENET_PATH and move validation images to labeled subfolders

  • The script may be helpful.

• Create a datasets subfolder and a symlink to the ImageNet dataset

$ ln -s PATH_TO_YOUR_IMAGENET ./data/

Folder of ImageNet Dataset:

data/ImageNet
├── train
├── val

Training two networks by MCL

python main_cifar.py --arch resnet18  --number-net 2 \
    --alpha 0.1 --gamma 1. --beta 0.1 --lam 1. 

More commands for training various architectures can be found in scripts/train_imagenet_mcl.sh

Results of MCL on ImageNet

We perform all experiments on a single NVIDIA Tesla V100 GPU (32GB) with three runs.

Training two networks by MCL combined with logit distillation

python main_cifar.py --arch resnet18  --number-net 2 \
    --alpha 0.1 --gamma 1. --beta 0.1 --lam 1. 

More commands for training various architectures can be found in scripts/train_imagenet_mcl.sh

Results of MCL combined with logit distillation on ImageNet

We perform all experiments on a single NVIDIA Tesla V100 GPU (32GB) with three runs.

Self-Supervised Learning on ImageNet dataset

Apply MCL(×2) to MoCo

python main_moco_mcl.py \
  -a resnet18 \
  --lr 0.03 \
  --batch-size 256 \
  --number-net 2 \
  --dist-url 'tcp://localhost:10001' \
  --multiprocessing-distributed \
  --world-size 1 \
  --rank 0 \
  --gpu-ids 0,1,2,3,4,5,6,7 

Linear Classification

python main_lincls.py \
  -a resnet18 \
  --lr 30.0 \
  --batch-size 256 \
  --pretrained [your checkpoint path]/checkpoint_0199.pth.tar \
  --dist-url 'tcp://localhost:10001' \
  --multiprocessing-distributed \
  --world-size 1 \
  --rank 0 \
  --gpu-ids 0,1,2,3,4,5,6,7 

Results of applying MCL to MoCo on ImageNet

We perform all experiments on 8 NVIDIA RTX 3090 GPUs with three runs.

Citation

@inproceedings{yang2022mcl,
  title={Mutual Contrastive Learning for Visual Representation Learning},
  author={Chuanguang Yang, Zhulin An, Linhang Cai, Yongjun Xu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}