Welcome to the BYOL (Bootstrap Your Own Latent) repository! This project provides a comprehensive, from-scratch implementation of BYOL, a revolutionary self-supervised learning algorithm. Whether youβre a researcher, developer, or enthusiast, this repository offers valuable insights into state-of-the-art unsupervised feature learning.
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Bootstrap Your Own Latent (BYOL) is a self-supervised learning algorithm that breaks new ground by learning from positive pairs (different augmentations of the same image) without relying on negative samples. This simplicity leads to faster training and competitive results across a range of benchmarks.
We evaluated our implementation using the STL10 Dataset, showcasing the impact of BYOL pretraining:
Training Method | Accuracy |
---|---|
Without Pretraining | 84.58% |
With BYOL Pretraining | 87.61% |
This demonstrates how BYOL can effectively enhance feature learning, even with relatively few epochs.
The STL10 Dataset is designed for unsupervised and self-supervised learning models, making it a perfect fit for BYOL.
Prerequisites
Clone the repository and install the required dependencies
@inproceedings{NEURIPS2020_f3ada80d,
author = {Grill, Jean-Bastien and Strub, Florian and Altch\'{e}, Florent and Tallec, Corentin and Richemond, Pierre and Buchatskaya, Elena and Doersch, Carl and Avila Pires, Bernardo and Guo, Zhaohan and Gheshlaghi Azar, Mohammad and Piot, Bilal and kavukcuoglu, koray and Munos, Remi and Valko, Michal},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {21271--21284},
publisher = {Curran Associates, Inc.},
title = {Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf},
volume = {33},
year = {2020}
}
This project is licensed under the MIT License. See the LICENSE file for details.