solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
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Updated
Nov 18, 2024 - Python
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
Self-Supervised Learning in PyTorch
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
PyTorch implementation of "Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning" with DDP and Apex AMP
Collections of self-supervised methods, based on cvpods.
SERAB: a multi-lingual benchmark for speech emotion recognition
Official PyTorch Implementation of Guarding Barlow Twins Against Overfitting with Mixed Samples
[NeurIPS 2023 (Spotlight)] Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
PyTorch Implementation of Self-Supervised Learning models
This repo reproduces results of "Understanding Self-Supervised Learning Dynamics without Contrastive Pairs" paper (https://arxiv.org/pdf/2102.06810.pdf)
A python library for self-supervised learning
Codebase for Imperial MSc AI Individual Project - Self-Supervised Learning for Audio Inference
Tracks VM and sole-tenant node usage for the purpose of license reporting
Bootstrap Your Own Latent (BYOL) in PyTorch
Simple Pytorch Implementation of BYOL: Bootstrap Your Own Latent(https://arxiv.org/abs/2006.07733) [Colab Version Available]
Codebase for Imperial MSc AI Group Project: How Well Do Self-Supervised Models Transfer to Medical Imaging?
Pytorch implementation of the paper, "Bootstrap Your Own Latent A New Approach to Self-Supervised Learning", or BYOL, https://arxiv.org/pdf/2006.07733v3.pdf
BYOL (Bootstrap Your Own Latent), implemented from scratch in Pytorch
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