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

[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]

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

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

Code for my Master's thesis on Multi‑Task Self‑Supervised Learning for label‑efficient learning. Modular PyTorch framework combining contrastive + pretext tasks with dynamic loss weighting, and centralized/federated training (HAR/STL‑10) to learn compact, robust representations.

[NN 2024] FullRot + WRMix: Any Region Can Be Perceived Equally and Effectively on Rotation Pretext Task Using Full Rotation and Weighted-Region Mixture

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

Overview of self-supervised video representation learning methods.

pretext-showcase magical innovation

Comparison of Feature Extraction Methods on Free-hand Sketches

An exploration of self-supervised and contrastive learning techniques (SimSiam) on CIFAR-10 dataset, comparing them against a supervised baseline in a low-data regime.

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

🔍 Implement SimSiam for self-supervised contrastive learning on CIFAR-10, comparing its performance against supervised and multi-task models.

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.