  The ability of deep neural networks to continually learn and adapt to a
sequence of tasks has remained challenging due to catastrophic forgetting of
previously learned tasks. Humans, on the other hand, have a remarkable ability
to acquire, assimilate, and transfer knowledge across tasks throughout their
lifetime without catastrophic forgetting. The versatility of the brain can be
attributed to the rehearsal of abstract experiences through a complementary
learning system. However, representation rehearsal in vision transformers lacks
diversity, resulting in overfitting and consequently, performance drops
significantly compared to raw image rehearsal. Therefore, we propose BiRT, a
novel representation rehearsal-based continual learning approach using vision
transformers. Specifically, we introduce constructive noises at various stages
of the vision transformer and enforce consistency in predictions with respect
to an exponential moving average of the working model. Our method provides
consistent performance gain over raw image and vanilla representation rehearsal
on several challenging CL benchmarks, while being memory efficient and robust
to natural and adversarial corruptions.
