Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
This addresses the challenge of non-stationary data for machine learning systems, offering a biologically inspired approach to continual learning.
The paper tackles the problem of catastrophic forgetting in continual learning by introducing a method that generates replay samples on the fly using the model's implicit memory, without external buffers or generators, and shows it is more efficient and scalable by specializing samples to each training batch.
Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective, without extraneous buffers or generator networks. Instead the implicit memory of learned samples within the assessed model itself is exploited. Furthermore, whereas existing work focuses on reinforcing the full seen data distribution, we show that optimizing for not forgetting calls for the generation of samples that are specialized to each real training batch, which is more efficient and scalable. We consider high-level parallels with the brain, notably the use of a single model for inference and recall, the dependency of recalled samples on the current environment batch, top-down modulation of activations and learning, abstract recall, and the dependency between the degree to which a task is learned and the degree to which it is recalled. These characteristics emerge naturally from the method without being controlled for.