Open-source alternatives to Probabilistic Contrastive Learning

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

IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark

CVPR 2024 Code for UNITE, an unsupervised approach for video domain adaptation (https://arxiv.org/abs/2312.02914)

Gradual Domain Adaptation via Self-Training of Auxiliary Models