Learning factorized representations for open-set domain adaptation

Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach
outperforms the state of the art in open-set domain adaptation.
Original languageEnglish
Title of host publicationInternational Conference on Learning Representations 2019
EditorsAlexander Rush
Place of PublicationLa Jolla CA USA
PublisherInternational Conference on Learning Representations (ICLR)
Number of pages11
ISBN (Print)9783800743629
Publication statusPublished - 2019
EventInternational Conference on Learning Representations 2019 - Ernest N. Morial Convention Center, New Orleans, United States of America
Duration: 6 May 20199 May 2019
Conference number: 7th
https://iclr.cc/
https://iclr.cc/Conferences/2019

Conference

ConferenceInternational Conference on Learning Representations 2019
Abbreviated titleICLR 2019
CountryUnited States of America
CityNew Orleans
Period6/05/199/05/19
OtherThe International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.
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