Meta channel masking for cross-domain few-shot image classification

Cross-domain Few-shot Learning (CD-FSL) aims to address the challenges of FSL where significant domain gaps exist between source and target image datasets. Unlike many existing CD-FSL methods that utilize an auxiliary target dataset with a few labeled target images to enhance model generalization, our approach directly tackles the limitations imposed by the reliance on source-specific knowledge. We observe that models trained on unbalanced datasets tend to overfit to source-specific features, which, while effective in the source domain, generalize poorly to the target image domain. To address this, we introduce a novel dropout-based framework named Meta Channel Masking (MCM). This framework attenuates the learning of model channels on the source domain by dynamically masking source feature channels during training. In contrast to traditional dropout techniques that manually set masking probabilities based on statistical assumptions about the source data, our MCM framework employs a meta-learning process that automatically adjusts channel mask probabilities. This adjustment is informed by auxiliary target data, effectively minimizing few-shot loss on the auxiliary target dataset and thereby enhancing the model's generalization capabilities in the target domain. Our extensive experiments across various image classification benchmark datasets demonstrate that our framework outperforms state-of-the-art methods.