Abstract
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.