SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation

Generalisation of deep neural networks becomes vulnerable when distribution shifts are encountered between train (source) and test (target) ___domain data. Few-shot ___domain adaptation mitigates this issue by adapting deep neural networks pre-trained on the source ___domain to the target ___domain using a randomly selected and annotated support set from the target ___domain. This paper argues that randomly selecting the support set can be further improved for effectively adapting the pre-trained source models to the target ___domain. Alternatively, we propose SelectNAdapt, an algorithm to curate the selection of the target ___domain samples, which are then annotated and included in the support set. In particular, for the K-shot adaptation problem, we first leverage self-supervision to learn features of the target ___domain data. Then, we propose a per-class clustering scheme of the learned target ___domain features and select K representative target samples using a distance-based scoring function. Finally, we bring our selection setup towards a practical ground by relying on pseudo-labels for clustering semantically similar target ___domain samples. Our experiments show promising results on three few-shot ___domain adaptation benchmarks for image recognition compared to related approaches and the standard random selection.