  Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for
handling large differences between the source and target domains--an important
concern in real-world scenarios. To overcome these large differences, recent
works have considered exploiting small-scale unlabeled data from the target
domain during the pre-training stage. This data enables self-supervised
pre-training on the target domain, in addition to supervised pre-training on
the source domain. In this paper, we empirically investigate which pre-training
is preferred based on domain similarity and few-shot difficulty of the target
domain. We discover that the performance gain of self-supervised pre-training
over supervised pre-training becomes large when the target domain is dissimilar
to the source domain, or the target domain itself has low few-shot difficulty.
We further design two pre-training schemes, mixed-supervised and two-stage
learning, that improve performance. In this light, we present six findings for
CD-FSL, which are supported by extensive experiments and analyses on three
source and eight target benchmark datasets with varying levels of domain
similarity and few-shot difficulty. Our code is available at
https://github.com/sungnyun/understanding-cdfsl.
