【导读】领域自适应(Domain Adaptation)是迁移学习(Transfer Learning)的一种,思路是将不同领域(如两个不同的数据集)的数据特征映射到同一个特征空间,这样可利用其它领域数据来增强目标领域训练。 本文整理了近几年期刊会议上领域自适应学习相关的论文与代码会议论文 Dhouib's: Revisiting epsilon, gamma, tau similarity learning for domain adaptation[NeurIPS2018](http://papers.nips.cc/paper/7969revisitingepsilongammatausimilaritylearningfordomainadaptation.pdf) CDAN: Conditional Adversarial Domain Adaptation[[NeurIPS2018]](http://papers.nips.cc/paper/7436conditionaladversarialdomainadaptation.pdf) Magliacane's: Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions[[NeurIPS2018]](http://papers.nips.cc/paper/8282domainadaptationbyusingcausalinferenc
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