  Learning new tasks accumulatively without forgetting remains a critical
challenge in continual learning. Generative experience replay addresses this
challenge by synthesizing pseudo-data points for past learned tasks and later
replaying them for concurrent training along with the new tasks' data.
Generative replay is the best strategy for continual learning under a strict
class-incremental setting when certain constraints need to be met: (i) constant
model size, (ii) no pre-training dataset, and (iii) no memory buffer for
storing past tasks' data. Inspired by the biological nervous system mechanisms,
we introduce a time-aware regularization method to dynamically fine-tune the
three training objective terms used for generative replay: supervised learning,
latent regularization, and data reconstruction. Experimental results on major
benchmarks indicate that our method pushes the limit of brain-inspired
continual learners under such strict settings, improves memory retention, and
increases the average performance over continually arriving tasks.
