  Continual zero-shot learning(CZSL) is a new domain to classify objects
sequentially the model has not seen during training. It is more suitable than
zero-shot and continual learning approaches in real-case scenarios when data
may come continually with only attributes for a few classes and attributes and
features for other classes. Continual learning(CL) suffers from catastrophic
forgetting, and zero-shot learning(ZSL) models cannot classify objects like
state-of-the-art supervised classifiers due to lack of actual data(or features)
during training. This paper proposes a novel continual zero-shot learning
(DVGR-CZSL) model that grows in size with each task and uses generative replay
to update itself with previously learned classes to avoid forgetting. We
demonstrate our hybrid model(DVGR-CZSL) outperforms the baselines and is
effective on several datasets, i.e., CUB, AWA1, AWA2, and aPY. We show our
method is superior in task sequentially learning with ZSL(Zero-Shot Learning).
We also discuss our results on the SUN dataset.
