How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark
Implementation of Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (ECCV 2020).
Code and data of the "Multi-domain adversarial learning" paper, Schoenauer-Sebag et al., accepted at ICLR 2019
Implementation of Graphical Modeling for Multi-Source Domain Adaptation (TPAMI).
The main features of francis0625/graphical-modeling-for-multi-source-domain-adaptation are: Multi-Source Adaptation.
Projects with overlapping indexed features include: altschulerwu-lab/mulann — Code and data of the "Multi-domain adversarial learning" paper, Schoenauer-Sebag et al., accepted at ICLR 2019. bupt-ai-cz/meta-selflearning — Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark. chrisallenming/ltc-msda — Implementation of Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (ECCV 2020). daoyuan98/mdda — Implementation of paper multi-source distilling domain adaptation. eddardd/wbtransport — (ICASSP'21/CVPR'21) Wasserstein Barycenter Transport. hcplab-sysu/msda.
