Continual Learning with Deep Generative Replay
This addresses memory limitations in AI systems learning multiple tasks, though it is an incremental improvement over replay-based methods.
The paper tackles catastrophic forgetting in continual learning by proposing Deep Generative Replay, a dual-model framework that samples past data without storing it, achieving competitive performance on image classification tasks.
Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.