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Starred repositories
Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function: A Minmax Perspective" (NCFM) in CVPR 2025 (Full Score, Highlight).
Awsome of VLM-CL. Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
This project aims to collect the latest "call for reviewers" links from various top CS/ML/AI conferences/journals
a collection of AWESOME things about Optimal Transport in Deep Learning
[TPAMI 2026] Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.
The official GitHub page for the survey paper "CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey". And this paper has been accepted for publication in IEEE TPAMI.
[CVPR 2025] "Towards Universal Soccer Video Understanding".
AirLLM 70B inference with single 4GB GPU
CVPR2023: AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Official Implementation for "HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks" (NeurIPS 2022)
Code for "They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning"
A repository and benchmark for online test-time adaptation.
[CVPR 2022] Official CoTTA Code for our paper Continual Test-Time Domain Adaptation
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
zhuohuangai / cafa-1
Forked from josephioos/cafaCode for ``NIPS 2021 universal semi-supervised learning''
Code release for Universal Domain Adaptation(CVPR 2019)
关于domain generalization,domain adaptation,causality,robutness,prompt,optimization,generative model各式各样研究的阅读笔记
A LaTeX resume template designed for optimal information density and aesthetic appeal.
optimizer & lr scheduler & loss function collections in PyTorch
torch-optimizer -- collection of optimizers for Pytorch
A collection of AWESOME things about domain adaptation