ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
This addresses the challenge of domain differences hindering adaptation in few-shot learning, but it is incremental as it builds on existing transfer learning approaches.
The paper tackles the problem of cross-domain few-shot learning by proposing a method that re-randomizes parameters before fine-tuning to reset source-specific information, resulting in improved few-shot performance.
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.