  Recently, zero-shot learning (ZSL) emerged as an exciting topic and attracted
a lot of attention. ZSL aims to classify unseen classes by transferring the
knowledge from seen classes to unseen classes based on the class description.
Despite showing promising performance, ZSL approaches assume that the training
samples from all seen classes are available during the training, which is
practically not feasible. To address this issue, we propose a more generalized
and practical setup for ZSL, i.e., continual ZSL (CZSL), where classes arrive
sequentially in the form of a task and it actively learns from the changing
environment by leveraging the past experience. Further, to enhance the
reliability, we develop CZSL for a single head continual learning setting where
task identity is revealed during the training process but not during the
testing. To avoid catastrophic forgetting and intransigence, we use knowledge
distillation and storing and replay the few samples from previous tasks using a
small episodic memory. We develop baselines and evaluate generalized CZSL on
five ZSL benchmark datasets for two different settings of continual learning:
with and without class incremental. Moreover, CZSL is developed for two types
of variational autoencoders, which generates two types of features for
classification: (i) generated features at output space and (ii) generated
discriminative features at the latent space. The experimental results clearly
indicate the single head CZSL is more generalizable and suitable for practical
applications.
