  Few-shot classification addresses the challenge of classifying examples given
only limited labeled data. A powerful approach is to go beyond data
augmentation, towards data synthesis. However, most of data
augmentation/synthesis methods for few-shot classification are overly complex
and sophisticated, e.g. training a wGAN with multiple regularizers or training
a network to transfer latent diversities from known to novel classes. We make
two contributions, namely we show that: (1) using a simple loss function is
more than enough for training a feature generator in the few-shot setting; and
(2) learning to generate tensor features instead of vector features is
superior. Extensive experiments on miniImagenet, CUB and CIFAR-FS datasets show
that our method sets a new state of the art, outperforming more sophisticated
few-shot data augmentation methods.
