Decoupled Self-Adaptive Distribution Regularization for Few-Shot Image Classification

Bingzhi Chen, Haoming Zhou, Yishu Liu, Biqing Zeng, Guangming Lu, Zheng Zhang

Abstract: The feature dispersion, arising from the inherent constraints of data scarcity, has emerged as a prominent challenge in the domain of few-shot learning. In this paper, we propose a novel Self-adaptive Distribution Regularization (SADR) approach, which can adaptively bridge the semantic gaps across distribution patterns and boundaries for learning from limited labeled data. Specifically, the technical core of our SADR approach is to decouple the feature embeddings into two discrete spaces: the intra-class and inter-class distributions, leading to robust and discriminative feature representations in a self-adaptive manner. To achieve meticulous similarity measurements while mitigating redundant feature information, an innovative regularized Brownian Distance Covariance (R-BDC) metric is strategically designed to simultaneously explore both the joint and marginal distributions present among diverse input samples. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority of our SADR approach over state-of-the-art baselines.