Joint Contrastive Learning with Infinite Possibilities

This is the implementation of 'Joint Contrastive Learning with Infinite Possibilities' [NeurIPS 2020]. The original paper can be found at https://arxiv.org/abs/2009.14776 .

Requirements

• torch
• torchvision

Datasets

• Download the ImageNet dataset from http://www.image-net.org.
• Then, and move validation images to labeled subfolders, using the following shell script.

Training

This implementation only supports multi-gpu, DistributedDataParallel training, which is faster and simpler; single-gpu or DataParallel training is not supported.

To do unsupervised pre-training of a ResNet-50 model, run:

bash scripts/main_pretrain.sh

Evaluation of linear classification

With a pre-trained model, to train a supervised linear classifier on frozen features/weights, run:

bash scripts/main_lincls.sh

Models

Our pre-trained ResNet-50 models can be downloaded from ResNet-50.

Citation

If you find this code or model useful for your research, please cite our paper:

@inproceedings{cai2020joint,
  title={Joint Contrastive Learning with Infinite Possibilities},
  author={Cai, Qi and Wang, Yu and Pan, Yingwei and Yao, Ting and Mei, Tao},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020}
}