TensorFlow implementation of Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction)

Last update: Sep 14, 2022

Overview

Barlow-Twins-TF

This repository implements Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction) in TensorFlow and demonstrates it on the CIFAR10 dataset.

Summary:

With a ResNet20 as a trunk and a 3-layer MLP (each layer containing 2048 units) and 100 epochs of pre-training, this training notebook can give 62.61% accuracy on the CIFAR10 test set. The pre-training total takes ~23 minutes on a single Tesla V100. There are minor differences from the original implementation. However, the original loss function and the other minor details like having a big enough projection dimension have been maintained.

For details on Barlow Twins, I suggest reading the original paper, it's really well-written.

Loss progress during pre-training

Other notes

Pre-trained model is available here.
To follow the original implementation details as closely as possible, a WarmUpCosine learning rate schedule has been used during pre-training:
During linear evaluation, Cosine Decay has been used.

Acknowledgements

Thanks to Stéphane Deny (one of the authors of the paper) for helping me catch a pesky bug.

Releases(v1.0.0)

v1.0.0(May 10, 2021)

The archive contains a ResNet20 encoder (trunk + projection head) trained using Barlow Twins for 100 epochs with CIFAR10.
Source code(tar.gz)
Source code(zip)
barlow_twins.tar.gz(38.07 MB)

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TensorFlow implementation of Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction)

Related tags

Overview

Barlow-Twins-TF

Loss progress during pre-training

Other notes

Acknowledgements

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