Attentive Prototype Few-Shot Learning with Capsule Network-Based Embedding

Few-shot learning, namely recognizing novel categories with a very small amount of training examples, is a challenging area of machine learning research. Traditional deep learning methods require massive training data to tune the huge number of parameters, which is often impractical and prone to over-fitting. In this work, we further research on the well-known few-shot learning method known as prototypical networks for better performance. Our contributions include (1) a new embedding structure to encode relative spatial relationships between features by applying a capsule network; (2) a new triplet loss designated to enhance the semantic feature embedding where similar samples are close to each other while dissimilar samples are farther apart; and (3) an effective non-parametric classifier termed attentive prototypes in place of the simple prototypes in current few-shot learning. The proposed attentive prototype aggregates all of the instances in a support class which are weighted by their importance, defined by the reconstruction error for a given query. The reconstruction error allows the classification posterior probability to be estimated, which corresponds to the classification confidence score. Extensive experiments on three benchmark datasets demonstrate that our approach is effective for the few-shot classification task.