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Cross-domain few-shot learning (CD-FSL), where there are few target samplesunder extreme differences between source and target domains, has recentlyattracted huge attention. Recent studies on CD-FSL generally focus on transferlearning based approaches, where a neural network is pre-trained on popularlabeled source domain datasets and then transferred to target domain data.Although the labeled datasets may provide suitable initial parameters for thetarget data, the domain difference between the source and target might hinderfine-tuning on the target domain. This paper proposes a simple yet powerfulmethod that re-randomizes the parameters fitted on the source domain beforeadapting to the target data. The re-randomization resets source-specificparameters of the source pre-trained model and thus facilitates fine-tuning onthe target domain, improving few-shot performance.

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning