Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration

Cian Eastwood, Ian Mason, Christopher K I Williams, Bernhard Schölkopf

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


Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain. We address these issues for a particularly pervasive type of domain shift called measurement shift which can be resolved by restoring the source features rather than extracting new ones. In particular, we propose Feature Restoration (FR) wherein we: (i) store a lightweight and flexible approximation of the feature distribution under the source data; and (ii) adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We additionally propose a bottom-up training scheme which boosts performance, which we call Bottom-Up Feature Restoration (BUFR). On real and synthetic data, we demonstrate that BUFR outperforms existing SFDA methods in terms of accuracy, calibration, and data efficiency, while being less reliant on the performance of the source model in the target domain.
Original languageEnglish
Title of host publicationProceedings of the Tenth International Conference on Learning Representations
Number of pages37
Publication statusPublished - 25 Apr 2022
EventTenth International Conference on Learning Representations 2022 - Virtual Conference
Duration: 25 Apr 202229 Apr 2022
Conference number: 10


ConferenceTenth International Conference on Learning Representations 2022
Abbreviated titleICLR 2022
Internet address


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