Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

This repository is mainly dedicated for listing the recent research advancements in the application of Self-Supervised-Learning in medical images computing field

Overview of unsupervised visual representation learning (or self-supervised learning, unsupervised pre-training) methods.

[TNSRE 2023] Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

A python implementation of “Self-Supervised Learning of Spatial Acoustic Representation with Cross-Channel Signal Reconstruction and Multi-Channel Conformer” [TASLP 2024]

[IEEE T-IP 2022] TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning

One-class classification approach using error of image transformation into one image

Official implementation of "Any Region Can Be Perceived Equally and Effectively on Rotation Pretext Task Using Full Rotation and Weighted-Region Mixture"

Comparison of Feature Extraction Methods on Free-hand Sketches

Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi

Overview of self-supervised video representation learning methods.

Pre-text pre-training -> image segmentaion pipelines. Utilize contrastive learning and ViT as Encoder. Studied the effects of dataset sizes, dataset similarity and effects of fine-tuning.