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Introduction

Official implementation of ICLR 2024 paper "Contrastive Learning Is Spectral Clustering On Similarity Graph" (https://arxiv.org/abs/2303.15103) .

Installation

Requirement:

  • Conda

Once installed conda, you can create the contrastive environment using conda env create -f environment.yaml.

Random Search

Just run python random_search.py

You can overwrite any pretraining arguments while random searching. For example, you want to random search the hyperparameters for CIFAR100 with lars optimizer in 100 epochs, you can run python random_search.py --dataset cifar100 --optimizer lars --max_epochs 100

For more details, see the argument help of random_search.py

Pretraining

Once you have got the best parameter by random search, you can run python simclr_module.py [args] to pretrain.

For more details, see the argument help of simclr_module.py.

Linear Probing

For linear probe, run python simclr_finetune.py --ckpt_path [path/to/your/ckpt] [args]

For more details, see the argument help of simclr_finetune.py. For most cases, you may only need to change dataset, data_dir, ckpt_path three arguments.

Acknowledgement

This repo is mainly based on Pytorch Lightning. Many thanks to their wonderful work!

Citations

Please cite the paper and star this repo if you use Kernel-InfoNCE and find it interesting/useful, thanks! Feel free to contact zhangyif21@mails.tsinghua.edu.cn | yangjq21@mails.tsinghua.edu.cn or open an issue if you have any questions.

@article{tan2023contrastive,
  title={Contrastive Learning Is Spectral Clustering On Similarity Graph},
  author={Tan, Zhiquan and Zhang, Yifan and Yang, Jingqin and Yuan, Yang},
  journal={arXiv preprint arXiv:2303.15103},
  year={2023}
}