Feature hallucination in hypersphere space for few-shot classification

Few-shot classification (FSC) targeting at classifying unseen classes with few labelled samples is still a challenging task. Recent works show that transfer-learning based approaches are competitive with meta-learning ones, which usually pre-train a convolutional neural networks (CNN)-based network using cross-entropy (CE) loss and throw away the last layer to post-process the novel classes. Hereby, they still suffer the issue of getting a more transferable extractor and lacking enough labelled novel samples. Thus, the authors propose the algorithm of feature hallucination in hypersphere space (FHHS) for FSC. On the first stage, the authors pre-train a more transferable feature extractor using a hypersphere loss (HL), which supplies CE with supervised contrastive (SC) loss and self-supervised loss (SSL), in which SC can map the base and novel images onto the hypersphere space densely. On the second stage, the authors generate new samples for unseen classes using their novel algorithm of synthetic novel sampling with the base (SNSB), which linearly interpolate between each novel class prototype and its K nearest neighbour base class prototypes. Comprehensive experiments on multiple popular FSC demonstrate that HL loss can enhance the performance of backbone network and the authors' feature hallucination method is superior to the existing hallucination-based methods.