CIBHash
source code for paper "Unsupervised Hashing with Contrastive Information Bottleneck" published in IJCAI 2021
Install / Use
/learn @zexuanqiu/CIBHashREADME
CIBHash
A Pytorch implementation of paper "Unsupervised Hashing with Contrastive Information Bottleneck "
Main Dependencies
- torch 1.4.0
- torchvision 0.5.0
- Pillow 5.4.1
- opencv-python 4.5.1.48
How to Run
# Run with the Cifar10 dataset
python main.py cifar16 --train --dataset cifar10 --encode_length 16 --cuda
If you run the above command, the program will download the Cifar10 dataset to the directory ./data/cifar10/ and then start to train.
Moreover, you can find the download link of NUS-WIDE dataset in this page; as for the MSCOCO dataset, you can directly visit the homepage to get the source data. You can refer to ./utils/data.py to get hints of preprocessing these two datasets.
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