Feature-Critic Networks for Heterogeneous Domain Generalisation

Yiying Li, Yongxin Yang, Wei Zhou, Timothy Hospedales

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of learning a model that generalises to unseen domains out of the box, and various approaches aim to train a domain-invariant feature extractor, typically by adding some manually designed losses. In this work, we propose a learning to learn approach, where the auxiliary loss that helps generalisation is itself learned. Beyond conventional domain generalisation, we consider a more challenging setting of heterogeneous domain generalisation, where the unseen domains do not share label space with the seen ones, and the goal is to train a feature representation that is useful off-the-shelf for novel data and novel categories. Experimental evaluation demonstrates that our method outperforms state-of-the-art solutions in both settings.
Original languageEnglish
Title of host publicationProceedings of the 36th International Conference on Machine Learning (ICML)
EditorsKamalika Chaudhuri, Ruslan Salakhutdinov
Place of PublicationLong Beach, USA
PublisherPMLR
Pages3915-3924
Number of pages10
Volume97
Publication statusE-pub ahead of print - 3 Jul 2019
EventThirty-sixth International Conference on Machine Learning - Long Beach Convention Center, Long Beach, United States
Duration: 9 Jun 201915 Jun 2019
Conference number: 36
https://icml.cc/Conferences/2019

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume97
ISSN (Electronic)2640-3498

Conference

ConferenceThirty-sixth International Conference on Machine Learning
Abbreviated titleICML 2019
CountryUnited States
CityLong Beach
Period9/06/1915/06/19
Internet address

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