Boosting Few-Shot Learning via Attentive Feature Regularization
Xingyu Zhu,
Shuo Wang,
Jinda Lu
et al.
Abstract:Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor. However, this mixing operation weakens the feature representation due to the linear interpolation and the overlooking of the importance of specific channels. To solve these issues, this paper proposes attentive feature regularization (AFR) which aims to improve the feature representativeness and d… Show more
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