Transferable Representation Learning with Deep Adaptation Networks

IEEE transactions on pattern analysis and machine intelligence, Volume 41, Issue 12, 2018, Pages 3071-3085.

Cited by: 82|Bibtex|Views83|DOI:https://doi.org/10.1109/TPAMI.2018.2868685
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de

Abstract:

Domain adaptation generalizes a learning machine across source domain and target domain under different distributions. Recent studies reveal that deep neural networks can learn transferable features generalizing well to similar novel tasks for domain adaptation. However, as deep features eventually transition from general to specific alon...More

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