  One of the most significant challenges facing a few-shot learning task is the
generalizability of the (meta-)model from the base to the novel categories.
Most of existing few-shot learning models attempt to address this challenge by
either learning the meta-knowledge from multiple simulated tasks on the base
categories, or resorting to data augmentation by applying various
transformations to training examples. However, the supervised nature of model
training in these approaches limits their ability of exploring variations
across different categories, thus restricting their cross-category
generalizability in modeling novel concepts. To this end, we present a novel
regularization mechanism by learning the change of feature representations
induced by a distribution of transformations without using the labels of data
examples. We expect this regularizer could expand the semantic space of base
categories to cover that of novel categories through the transformation of
feature representations. It could minimize the risk of overfitting into base
categories by inspecting the transformation-augmented variations at the encoded
feature level. This results in the proposed FLAT (Few-shot Learning via
Autoencoding Transformations) approach by autoencoding the applied
transformations. The experiment results show the superior performances to the
current state-of-the-art methods in literature.
