SG-FSL: Cross-Domain Few-Shot Learning with Style-Decoupled Augmentation and Gradient-Conflict Adjustment

摘要

Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge acquired from a source domain with abundant data to the target domain with limited labeled samples. Recent advancements have enhanced model generalization through Perturbation Augmentation (PA), facilitating more effective knowledge transfer. However, PA-based CD-FSL methods still suffer from two critical challenges, i.e., (1) limited diversity of augmented samples, making it difficult to cover the true distribution of unseen domains, and (2) conflicting gradients during model optimization, where augmented and original samples drive the model's optimization in opposing directions. To address these issues, we propose a novel PA-based framework with Style-Decoupled Augmentation (SDA) and Gradient-Conflict Adjustment (GCA) for Cross-Domain Few-Shot Learning, which is termed ''SG-FSL''. Specifically, SDA decouples the source domain style into style weights and basis styles, generating diverse unseen styles by perturbing the style weights to reweight the basis styles. Meanwhile, GCA leverages the angular relationships between the domain-specific gradient directions of augmented and original features, adaptively adjusting the gradient directions of original features to ensure that the model acquires diverse domain knowledge without interference, guiding it toward conflict-free optimization. Comprehensive experiments on multiple benchmark datasets consistently demonstrate the effectiveness and superiority of our method over state-of-the-art baselines.