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.
Code and data of the "Multi-domain adversarial learning" paper, Schoenauer-Sebag et al., accepted at ICLR 2019
The main features of altschulerwu-lab/mulann are: Multi-Source Adaptation.
Projects with overlapping indexed features include: 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. francis0625/graphical-modeling-for-multi-source-domain-adaptation — Implementation of Graphical Modeling for Multi-Source Domain Adaptation (TPAMI). hcplab-sysu/msda.
Implementation of Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (ECCV 2020).
Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark
