  This paper proposes a multi-layer neural network structure for few-shot image
recognition of novel categories. The proposed multi-layer neural network
architecture encodes transferable knowledge extracted from a large annotated
dataset of base categories. This architecture is then applied to novel
categories containing only a few samples. The transfer of knowledge is carried
out at the feature-extraction and the classification levels distributed across
the two training stages. In the first-training stage, we introduce the relative
feature to capture the structure of the data as well as obtain a
low-dimensional discriminative space. Secondly, we account for the variable
variance of different categories by using a network to predict the variance of
each class. Classification is then performed by computing the Mahalanobis
distance to the mean-class representation in contrast to previous approaches
that used the Euclidean distance. In the second-training stage, a
category-agnostic mapping is learned from the mean-sample representation to its
corresponding class-prototype representation. This is because the mean-sample
representation may not accurately represent the novel category prototype.
Finally, we evaluate the proposed network structure on four standard few-shot
image recognition datasets, where our proposed few-shot learning system
produces competitive performance compared to previous work. We also extensively
studied and analyzed the contribution of each component of our proposed
framework.
