  Deep Neural networks forget previously learnt tasks when they are faced with
learning new tasks. This is called catastrophic forgetting. Rehearsing the
neural network with the training data of the previous task can protect the
network from catastrophic forgetting. Since rehearsing requires the storage of
entire previous data, Pseudo rehearsal was proposed, where samples belonging to
the previous data are generated synthetically for rehearsal. In an image
classification setting, while current techniques try to generate synthetic data
that is photo-realistic, we demonstrated that Neural networks can be rehearsed
on data that is not photo-realistic and still achieve good retention of the
previous task. We also demonstrated that forgoing the constraint of having
photo realism in the generated data can result in a significant reduction in
the consumption of computational and memory resources for pseudo rehearsal.
