  Classical deep neural networks are limited in their ability to learn from
emerging streams of training data. When trained sequentially on new or evolving
tasks, their performance degrades sharply, making them inappropriate in
real-world use cases. Existing methods tackle it by either storing old data
samples or only updating a parameter set of DNNs, which, however, demands a
large memory budget or spoils the flexibility of models to learn the
incremented class distribution. In this paper, we shed light on an on-call
transfer set to provide past experiences whenever a new class arises in the
data stream. In particular, we propose a Zero-Shot Incremental Learning not
only to replay past experiences the model has learned but also to perform this
in a zero-shot manner. Towards this end, we introduced a memory recovery
paradigm in which we query the network to synthesize past exemplars whenever a
new task (class) emerges. Thus, our method needs no fixed-sized memory, besides
calls the proposed memory recovery paradigm to provide past exemplars, named a
transfer set in order to mitigate catastrophically forgetting the former
classes. Moreover, in contrast with recently proposed methods, the suggested
paradigm does not desire a parallel architecture since it only relies on the
learner network. Compared to the state-of-the-art data techniques without
buffering past data samples, ZS-IL demonstrates significantly better
performance on the well-known datasets (CIFAR-10, Tiny-ImageNet) in both
Task-IL and Class-IL settings.
