  We present a generative framework for generalized zero-shot learning where
the training and test classes are not necessarily disjoint. Built upon a
variational autoencoder based architecture, consisting of a probabilistic
encoder and a probabilistic conditional decoder, our model can generate novel
exemplars from seen/unseen classes, given their respective class attributes.
These exemplars can subsequently be used to train any off-the-shelf
classification model. One of the key aspects of our encoder-decoder
architecture is a feedback-driven mechanism in which a discriminator (a
multivariate regressor) learns to map the generated exemplars to the
corresponding class attribute vectors, leading to an improved generator. Our
model's ability to generate and leverage examples from unseen classes to train
the classification model naturally helps to mitigate the bias towards
predicting seen classes in generalized zero-shot learning settings. Through a
comprehensive set of experiments, we show that our model outperforms several
state-of-the-art methods, on several benchmark datasets, for both standard as
well as generalized zero-shot learning.
