LightlyTrain is the first PyTorch framework to pretrain computer vision models on unlabeled data for industrial applications

[ECCV2024] Video Foundation Models & Data for Multimodal Understanding

Self-supervised decorrelation-denoising of X-ray radiographs

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

[ICCV 2025] Official PyTorch Implementation of "Learning Self-supervised Part-aware 3D Hybrid Representations of 2D Gaussians and Superquadrics"

[TIP 2025] DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation

This is the implementation of DCMVC: Dual Contrastive Multi-view Clustering, published in Neurocomputing 2025.

[RA-L 25] Self-Supervised Diffusion-Based Scene Flow Estimation and Motion Segmentation with 4D Radar

Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection

🏠 [JBHI 2024] Pytorch implementation of 'MonoLoT: Self-Supervised Monocular Depth Estimation in Low-Texture Scenes for Automatic Robotic Endoscopy'

TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction (2025)

[ECCV 2024] Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation

Wav2vec 2.0 Self-Supervised Pretraining

[ECCV 2024] This is the official code for the paper "Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations"

A self-supervised network for image denoising and watermark removal (Neural Networks 2024)

Official implementation of "Augmentation-aware Self-supervised Learning with Conditioned Projector"

Efficient computing methods developed by Huawei Noah's Ark Lab

Official PyTroch implementation for Learning from Memory: A Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features

Library to perform image and video self-supervised learning.

Introduction page of the coming tutorial "Self-Supervised Learning in Recommendation: Fundamentals and Advances" at The Web Conference (WWW) 2022