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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
Pytorch Code release for our NeurIPS paper "Multi-source Domain Adaptation for Semantic Segmentation"
The main features of luodian/madan 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. francis0625/graphical-modeling-for-multi-source-domain-adaptation — Implementation of Graphical Modeling for Multi-Source Domain Adaptation (TPAMI).
