Official repository of the paper Whitening for Self-Supervised Representation Learning
arXiv:2007.06346
It includes 3 types of losses:
- W-MSE arXiv
- Contrastive SimCLR arXiv
- BYOL arXiv
And 5 datasets:
- CIFAR-10 and CIFAR-100
- STL-10
- Tiny ImageNet
- ImageNet-100
Checkpoints are stored in data each 100 epochs during training.
The implementation is optimized for a single GPU, although multiple are also supported. It includes fast evaluation: we pre-compute embeddings for the entire dataset and then train a classifier on top. The evaluation of the ResNet-18 encoder takes about one minute.
The implementation is based on PyTorch. Logging works on wandb.ai. See docker/Dockerfile.
To get this dataset, take the original ImageNet and filter out this subset of classes. We do not use augmentations during testing, and loading big images with resizing on the fly is slow, so we can preprocess classifier train and test images. We recommend mogrify for it. First, you need to resize to 256 (just like torchvision.transforms.Resize(256)) and then crop to 224 (like torchvision.transforms.CenterCrop(224)). Finally, put the original images to train, and resized to clf and test.
Detailed settings are good by default, to see all options:
python -m train --help
python -m test --help
To reproduce the results from table 1:
python -m train --dataset cifar10 --epoch 1000 --lr 3e-3 --num_samples 4 --bs 256 --emb 64 --w_size 128
python -m train --dataset cifar100 --epoch 1000 --lr 3e-3 --num_samples 4 --bs 256 --emb 64 --w_size 128
python -m train --dataset stl10 --epoch 2000 --lr 2e-3 --num_samples 4 --bs 256 --emb 128 --w_size 256
python -m train --dataset tiny_in --epoch 1000 --lr 2e-3 --num_samples 4 --bs 256 --emb 128 --w_size 256
python -m train --dataset cifar10 --epoch 1000 --lr 3e-3 --emb 64 --w_size 128
python -m train --dataset cifar100 --epoch 1000 --lr 3e-3 --emb 64 --w_size 128
python -m train --dataset stl10 --epoch 2000 --lr 2e-3 --emb 128 --w_size 256 --w_iter 4
python -m train --dataset tiny_in --epoch 1000 --lr 2e-3 --emb 128 --w_size 256 --w_iter 4
python -m train --dataset cifar10 --epoch 1000 --lr 3e-3 --emb 64 --method contrastive
python -m train --dataset cifar100 --epoch 1000 --lr 3e-3 --emb 64 --method contrastive
python -m train --dataset stl10 --epoch 2000 --lr 2e-3 --emb 128 --method contrastive
python -m train --dataset tiny_in --epoch 1000 --lr 2e-3 --emb 128 --method contrastive
python -m train --dataset cifar10 --epoch 1000 --lr 3e-3 --emb 64 --method byol
python -m train --dataset cifar100 --epoch 1000 --lr 3e-3 --emb 64 --method byol
python -m train --dataset stl10 --epoch 2000 --lr 2e-3 --emb 128 --method byol
python -m train --dataset tiny_in --epoch 1000 --lr 2e-3 --emb 128 --method byol
python -m train --dataset imagenet --epoch 240 --lr 2e-3 --emb 128 --w_size 256 --crop_s0 0.08 --cj0 0.8 --cj1 0.8 --cj2 0.8 --cj3 0.2 --gs_p 0.2
python -m train --dataset imagenet --epoch 240 --lr 2e-3 --num_samples 4 --bs 256 --emb 128 --w_size 256 --crop_s0 0.08 --cj0 0.8 --cj1 0.8 --cj2 0.8 --cj3 0.2 --gs_p 0.2
Use --no_norm to disable normalization (for Euclidean distance).
@article{ermolov2020whitening,
title={Whitening for Self-Supervised Representation Learning},
author={Aleksandr Ermolov and Aliaksandr Siarohin and Enver Sangineto and Nicu Sebe},
journal={arXiv preprint arXiv:2007.06346},
year={2020}
}
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