Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach

Minyoung Kim, Da Li, Timothy M Hospedales

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

Abstract / Description of output

We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case `target' domain and adapt a model to that worst-case domain, thereby improving the model’s robustness. To synthesise data that is challenging yet semantics-preserving, we generate Fourier amplitude images and combine them with source domain phase images, exploiting the widely believed conjecture from signal processing that amplitude spectra mainly determines image style, while phase data mainly captures image semantics. To synthesise a worst-case domain for adaptation, we train the classifier and the amplitude generator adversarially. Specifically, we exploit the maximum classifier discrepancy (MCD) principle from DA that relates the target domain performance to the discrepancy of classifiers in the model hypothesis space. By Bayesian hypothesis modeling, we express the model hypothesis space effectively as a posterior distribution over classifiers given the source domains, making adversarial MCD minimisation feasible. On the DomainBed benchmark including the large-scale DomainNet dataset, the proposed approach yields significantly improved domain generalisation performance over the state-of-the-art.
Original languageEnglish
Title of host publicationThe Eleventh International Conference on Learning Representations
Subtitle of host publicationICLR 2023
Number of pages21
Publication statusPublished - 1 May 2023
EventThe Eleventh International Conference on Learning Representations - Kigali, Rwanda
Duration: 1 May 20235 May 2023
https://iclr.cc/Conferences/2023

Conference

ConferenceThe Eleventh International Conference on Learning Representations
Abbreviated titleICLR 2023
Country/TerritoryRwanda
CityKigali
Period1/05/235/05/23
Internet address

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