This repository Self, Semi Supervised Learning for Image Representation

The code for "Text-to-image synthesis with self-supervised learning"

JAX/Flax implementation of "VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning". ICLR 2022. https://arxiv.org/abs/2105.04906

Python library for self-supervised one-shot learning for automatic segmentation of GAN-generated images.

This is a playground for learning and working self-supervised learning and its applications.

The aim is not only to make a model which can classify the chest x-ray data but also to extract the heat maps of the x-ray for faster diagnosis and making it more reliable to use in real time.

Litechess is a low resource neural chess engine developed in Python (using Pytorch for Deep Learning).

Official pytorch implementation for FEN (Feature Enhancement Network)

self-supervised learning

Specific domains implemenation for ML. Including CV, NLP, Bioinformatics.

this is a repo to record the practice of self-supervised learning and some resources found on the networks

Visualization methods to interpret CNNs and Vision Transformers, trained in a supervised or self-supervised way. The methods are based on CAM or on the attention mechanism of Transformers. The results are evaluated qualitatively and quantitatively.

Experimenting with various autoencoder architectures.

A pytorch lightning implementation of VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

BYOL 기반 음식 사진 인식 모델 구축

This project explores the use of latent variable models for self-supervised anomaly detection in neuroimaging data.

21st IEEE International Conference on Industrial Informatics - INDIN2023. solo-learn: a library of self-supervised methods for visual representation learning and our paper titled: Self-supervised Learning with Temporary Predictions: Linear Projection

A comprehensive list of awesome self-supervised speech representation learning papers.

Hierarchical Clustering of Leukemia Gene Expression Dataset

Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis