  We present a domain adaptation based generative framework for zero-shot
learning. Our framework addresses the problem of domain shift between the seen
and unseen class distributions in zero-shot learning and minimizes the shift by
developing a generative model trained via adversarial domain adaptation. Our
approach is based on end-to-end learning of the class distributions of seen
classes and unseen classes. To enable the model to learn the class
distributions of unseen classes, we parameterize these class distributions in
terms of the class attribute information (which is available for both seen and
unseen classes). This provides a very simple way to learn the class
distribution of any unseen class, given only its class attribute information,
and no labeled training data. Training this model with adversarial domain
adaptation further provides robustness against the distribution mismatch
between the data from seen and unseen classes. Our approach also provides a
novel way for training neural net based classifiers to overcome the hubness
problem in zero-shot learning. Through a comprehensive set of experiments, we
show that our model yields superior accuracies as compared to various
state-of-the-art zero shot learning models, on a variety of benchmark datasets.
Code for the experiments is available at github.com/vkkhare/ZSL-ADA
