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
Code released for CVPR 2019 paper "Learning to Transfer Examples for Partial Domain Adaptation"
Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation"