Abstract
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, characterized by a change in measurement system (e.g. a change in sensor or lighting). In the source domain, we store a lightweight and flexible approximation of the feature distribution under the source data. In the target domain, we adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We call this method Feature Restoration (FR) as it seeks to extract features with the same semantics from the target domain as were previously extracted from the source. We additionally propose Bottom-Up Feature Restoration (BUFR), a bottom-up training scheme for FR which boosts performance by preserving learnt structure in the later layers of a network. Through experiments we demonstrate that BUFR often 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.
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URL
https://arxiv.org/abs/2107.05446
