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CGHash: Conditional Generative Model enhanced Contrastive Hashing

A pytorch implementation of our paper:

Unsupervised Hashing with Contrastive Learning by Exploiting Similarity Knowledge and Hidden Structure of Data

Accepted in Proceedings of the 31st ACM International Conference on Multimedia [ACM MM 2023].

Training Schema

Settings

  1. Place downloaded datasets at datasets/. (Cifar-10 will be downloaded automatically.)
  2. [Optional] Refer to utils/data_path.py to set dataset path.
  3. Place pre-trained models at models/pretrained_backbones/. (A 300-epoch SimCLR pre-trained models for Cifar-10 is available here.)
  4. Configure training details in configs/[dataset]/[stage]_[dataset]_[code_length].yml.

Training Example for 64-bit Cifar-10

  1. Place Cifar-10 dataset at datasets/ manually or download automatically later.

  2. Configure training details in configs/cifar-10/mine_cifar10.yml, configs/cifar-10/cghash_cifar10_64.yml, configs/cifar-10/selflabel_cifar10_64.yml.

  3. Run shell training script.

    chmod +x ./run.sh
./run.sh [CODE_LENGTH] [GPU_ID]
# e.g. ./run.sh 64 0,1

Retrieval Example