Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

OpenMMLab Pre-training Toolbox and Benchmark

A python library for self-supervised learning on images.

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

Self-Supervised Speech Pre-training and Representation Learning Toolkit

The official repo for [NeurIPS'22] "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation" and [TPAMI'23] "ViTPose++: Vision Transformer for Generic Body Pose Estimation"

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

A library for graph deep learning research

An all-in-one toolkit for computer vision

Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.

solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning

A Semantic Controllable Self-Supervised Learning Framework to learn general human representations from massive unlabeled human images, which can benefit downstream human-centric tasks to the maximum extent

SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

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