Optimal Transport for Domain Adaptation

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 39; no. 9; pp. 1853 - 1865
• Main Authors: Courty, Nicolas, Flamary, Remi, Tuia, Devis, Rakotomamonjy, Alain
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Adaptation; Alignment; Analytics; Case depth; classification; Computer Science; Data analysis; Feature extraction; Invariants; Kernel; Machine Learning; optimal transport; Probability density function; Probability distribution; Representations; Robustness (mathematics); Signal and Image Processing; Statistics; Training; transfer learning; Transportation; Unsupervised domain adaptation; visual adaptation; Visual observation
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2016.2615921

Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.