[Re] Bootstrap Your Own Latent: A new approach to self-supervised learning

[ADevillers]

Original article: J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo,
and M. G. Azar. “Bootstrap your own latent: A new approach to self-supervised learning.” In: arXiv preprint
arXiv:2006.07733 (2020)

PDF URL: https://github.com/ADevillers/BYOL/blob/main/report.pdf
Metadata URL: https://github.com/ADevillers/BYOL/blob/main/report.metadata.tex
Code URL: https://github.com/ADevillers/BYOL/tree/main

Scientific domain: Representation Learning
Programming language: Python
Suggested editor: @rougier

[rougier]

Thansk for your submission and sorry for the delay, we'll assign an editor soon.

[rougier]

@gdetor @benoit-girard @koustuvsinha Can any of you edit this submission?

[benoit-girard]

I can handle this one too (I'll try not to mix up between the two consecutive submissions...).

[benoit-girard]

@ADevillers : given the fact that you results were obtained using HPC, that the reviewers may not have access to, are there ways to test you code without it (like running only a subpart of it...)?

(same question applies to the companion paper)

[ADevillers]

Yes, you can train on CIFAR10 or perform linear evaluation on CIFAR10 and ImageNet with the following setup:

1. Ensure you use --computer='other'.
2. For GPU usage (recommended for faster processing), set --hardware='mono-gpu'. If you're using a CPU, set --hardware='cpu'.
3. Follow the commands provided in the README's final section.

Note: Training on ImageNet without HPC is impractical due to the large memory requirements for the necessary batch size and the extended duration it might take; yet, evaluation may be fine.

[benoit-girard]

Good news: @charlypg has accepted to review this paper and its companion!