AMMD: Attentive maximum mean discrepancy for few-shot image classification

Metric-based methods have attained promising performance for few-shot image classification. Maximum Mean Discrepancy (MMD) is a typical distance between distributions, requiring to compute expectations w.r.t. data distributions. In this paper, we propose Attentive Maximum Mean Discrepancy (AMMD) to measure the distances between query images and support classes for few-shot classification. Each query image is classified as the support class with minimal AMMD distance. The proposed AMMD assists MMD with distributions adaptively estimated by an Attention-based Distribution Generation Module (ADGM). ADGM is learned to put more mass on more discriminative features, which makes the proposed AMMD distance emphasize discriminative features and overlook spurious features. Extensive experiments show that our AMMD achieves competitive or state-of-the-art performance on multiple few-shot classification benchmark datasets. Code is available at https://github.com/WuJi1/AMMD.