  Cross-domain few-shot learning (CD-FSL), where there are few target samples
under extreme differences between source and target domains, has recently
attracted huge attention. Recent studies on CD-FSL generally focus on transfer
learning based approaches, where a neural network is pre-trained on popular
labeled source domain datasets and then transferred to target domain data.
Although the labeled datasets may provide suitable initial parameters for the
target data, the domain difference between the source and target might hinder
fine-tuning on the target domain. This paper proposes a simple yet powerful
method that re-randomizes the parameters fitted on the source domain before
adapting to the target data. The re-randomization resets source-specific
parameters of the source pre-trained model and thus facilitates fine-tuning on
the target domain, improving few-shot performance.
