  A major obstacle to developing artificial intelligence applications capable
of true lifelong learning is that artificial neural networks quickly or
catastrophically forget previously learned tasks when trained on a new one.
Numerous methods for alleviating catastrophic forgetting are currently being
proposed, but differences in evaluation protocols make it difficult to directly
compare their performance. To enable more meaningful comparisons, here we
identified three distinct scenarios for continual learning based on whether
task identity is known and, if it is not, whether it needs to be inferred.
Performing the split and permuted MNIST task protocols according to each of
these scenarios, we found that regularization-based approaches (e.g., elastic
weight consolidation) failed when task identity needed to be inferred. In
contrast, generative replay combined with distillation (i.e., using class
probabilities as "soft targets") achieved superior performance in all three
scenarios. Addressing the issue of efficiency, we reduced the computational
cost of generative replay by integrating the generative model into the main
model by equipping it with generative feedback or backward connections. This
Replay-through-Feedback approach substantially shortened training time with no
or negligible loss in performance. We believe this to be an important first
step towards making the powerful technique of generative replay scalable to
real-world continual learning applications.
