  The ability to learn different tasks sequentially is essential to the
development of artificial intelligence. In general, neural networks lack this
capability, the major obstacle being catastrophic forgetting. It occurs when
the incrementally available information from non-stationary data distributions
is continually acquired, disrupting what the model has already learned. Our
approach remembers old tasks by projecting the representations of new tasks
close to that of old tasks while keeping the decision boundaries unchanged. We
employ the center loss as a regularization penalty that enforces new tasks'
features to have the same class centers as old tasks and makes the features
highly discriminative. This, in turn, leads to the least forgetting of already
learned information. This method is easy to implement, requires minimal
computational and memory overhead, and allows the neural network to maintain
high performance across many sequentially encountered tasks. We also
demonstrate that using the center loss in conjunction with the memory replay
outperforms other replay-based strategies. Along with standard MNIST variants
for continual learning, we apply our method to continual domain adaptation
scenarios with the Digits and PACS datasets. We demonstrate that our approach
is scalable, effective, and gives competitive performance compared to
state-of-the-art continual learning methods.
