  Deep neural networks have shown promise in several domains, and the learned
data (task) specific information is implicitly stored in the network
parameters. Extraction and utilization of encoded knowledge representations are
vital when data is no longer available in the future, especially in a continual
learning scenario. In this work, we introduce {\em flashcards}, which are
visual representations that {\em capture} the encoded knowledge of a network as
a recursive function of predefined random image patterns. In a continual
learning scenario, flashcards help to prevent catastrophic forgetting and
consolidating knowledge of all the previous tasks. Flashcards need to be
constructed only before learning the subsequent task, and hence, independent of
the number of tasks trained before. We demonstrate the efficacy of flashcards
in capturing learned knowledge representation (as an alternative to the
original dataset) and empirically validate on a variety of continual learning
tasks: reconstruction, denoising, task-incremental learning, and new-instance
learning classification, using several heterogeneous benchmark datasets.
Experimental evidence indicates that: (i) flashcards as a replay strategy is {
\em task agnostic}, (ii) performs better than generative replay, and (iii) is
on par with episodic replay without additional memory overhead.
