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This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a framework for aligning feature distributions between source and target datasets, a toolkit for domain generalization, and a library for semi-supervised learning using small labeled datasets and large unlabeled sets. The library provides specialized capabilities for unsupervised domain adaptation, including the use of adversarial networks, discrepancy-based architectures, and image-to-image translation to reduce distribution mismatch. It also includes tools for domain generali
Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark
CVPR 2020 Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch)
IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm
[CVPR 2024] Code for UNITE, an unsupervised approach for video domain adaptation (https://arxiv.org/abs/2312.02914)
The main features of reddyav1/unite are: Self Training Methods, Video Domain Adaptation.
Projects with overlapping indexed features include: thuml/transfer-learning-library — This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a… bupt-ai-cz/iast-eccv2020 — IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020)… bupt-ai-cz/meta-selflearning — Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark. cmhungsteve/sstda — [CVPR 2020] Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch). ldkong1205/transvae — [NeurIPS 2023] Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective. ljjcoder/probabilistic-contrastive-learning — Probabilistic Contrastive Learning for Domain Adaptation.