  Learning to classify unseen class samples at test time is popularly referred
to as zero-shot learning (ZSL). If test samples can be from training (seen) as
well as unseen classes, it is a more challenging problem due to the existence
of strong bias towards seen classes. This problem is generally known as
\emph{generalized} zero-shot learning (GZSL). Thanks to the recent advances in
generative models such as VAEs and GANs, sample synthesis based approaches have
gained considerable attention for solving this problem. These approaches are
able to handle the problem of class bias by synthesizing unseen class samples.
However, these ZSL/GZSL models suffer due to the following key limitations:
$(i)$ Their training stage learns a class-conditioned generator using only
\emph{seen} class data and the training stage does not \emph{explicitly} learn
to generate the unseen class samples; $(ii)$ They do not learn a generic
optimal parameter which can easily generalize for both seen and unseen class
generation; and $(iii)$ If we only have access to a very few samples per seen
class, these models tend to perform poorly. In this paper, we propose a
meta-learning based generative model that naturally handles these limitations.
The proposed model is based on integrating model-agnostic meta learning with a
Wasserstein GAN (WGAN) to handle $(i)$ and $(iii)$, and uses a novel task
distribution to handle $(ii)$. Our proposed model yields significant
improvements on standard ZSL as well as more challenging GZSL setting. In ZSL
setting, our model yields 4.5\%, 6.0\%, 9.8\%, and 27.9\% relative improvements
over the current state-of-the-art on CUB, AWA1, AWA2, and aPY datasets,
respectively.
