  Contemporary state-of-the-art approaches to Zero-Shot Learning (ZSL) train
generative nets to synthesize examples conditioned on the provided metadata.
Thereafter, classifiers are trained on these synthetic data in a supervised
manner. In this work, we introduce Z2FSL, an end-to-end generative ZSL
framework that uses such an approach as a backbone and feeds its synthesized
output to a Few-Shot Learning (FSL) algorithm. The two modules are trained
jointly. Z2FSL solves the ZSL problem with a FSL algorithm, reducing, in
effect, ZSL to FSL. A wide class of algorithms can be integrated within our
framework. Our experimental results show consistent improvement over several
baselines. The proposed method, evaluated across standard benchmarks, shows
state-of-the-art or competitive performance in ZSL and Generalized ZSL tasks.
