  When labeled training data is scarce, a promising data augmentation approach
is to generate visual features of unknown classes using their attributes. To
learn the class conditional distribution of CNN features, these models rely on
pairs of image features and class attributes. Hence, they can not make use of
the abundance of unlabeled data samples. In this paper, we tackle any-shot
learning problems i.e. zero-shot and few-shot, in a unified feature generating
framework that operates in both inductive and transductive learning settings.
We develop a conditional generative model that combines the strength of VAE and
GANs and in addition, via an unconditional discriminator, learns the marginal
feature distribution of unlabeled images. We empirically show that our model
learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA
and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e.
inductive and transductive (generalized) zero- and few-shot learning settings.
We also demonstrate that our learned features are interpretable: we visualize
them by inverting them back to the pixel space and we explain them by
generating textual arguments of why they are associated with a certain label.
