A Meta-Learned Neuron model for Continual Learning

TL;DR

This paper introduces a meta-learned neuron model designed for continual learning, capable of memorizing sequences of data and retaining knowledge without forgetting, regardless of task structure or data correlation.

Contribution

It proposes replacing standard neurons with meta-learned neurons optimized to minimize catastrophic interference, enabling domain-general continual learning without task-specific assumptions.

Findings

Memorizes dataset-length sequences of training samples.

Generalizes learning capabilities across various domains.

Does not rely on task construction or data correlation assumptions.

Abstract

Continual learning is the ability to acquire new knowledge without forgetting the previously learned one, assuming no further access to past training data. Neural network approximators trained with gradient descent are known to fail in this setting as they must learn from a stream of data-points sampled from a stationary distribution to converge. In this work, we replace the standard neuron by a meta-learned neuron model whom inference and update rules are optimized to minimize catastrophic interference. Our approach can memorize dataset-length sequences of training samples, and its learning capabilities generalize to any domain. Unlike previous continual learning methods, our method does not make any assumption about how tasks are constructed, delivered and how they relate to each other: it simply absorbs and retains training samples one by one, whether the stream of input data is time-correlated or not.