  Zero-shot learning is a new paradigm to classify objects from classes that
are not available at training time. Zero-shot learning (ZSL) methods have
attracted considerable attention in recent years because of their ability to
classify unseen/novel class examples. Most of the existing approaches on ZSL
works when all the samples from seen classes are available to train the model,
which does not suit real life. In this paper, we tackle this hindrance by
developing a generative replay-based continual ZSL (GRCZSL). The proposed
method endows traditional ZSL to learn from streaming data and acquire new
knowledge without forgetting the previous tasks' gained experience. We handle
catastrophic forgetting in GRCZSL by replaying the synthetic samples of seen
classes, which have appeared in the earlier tasks. These synthetic samples are
synthesized using the trained conditional variational autoencoder (VAE) over
the immediate past task. Moreover, we only require the current and immediate
previous VAE at any time for training and testing. The proposed GRZSL method is
developed for a single-head setting of continual learning, simulating a
real-world problem setting. In this setting, task identity is given during
training but unavailable during testing. GRCZSL performance is evaluated on
five benchmark datasets for the generalized setup of ZSL with fixed and dynamic
(incremental class) settings of continual learning. The existing class setting
presented recently in the literature is not suitable for a class-incremental
setting. Therefore, this paper proposes a new setting to address this issue.
Experimental results show that the proposed method significantly outperforms
the baseline and the state-of-the-art method and makes it more suitable for
real-world applications.
