Continual Learning of Context-dependent Processing in Neural Networks

TL;DR

This paper introduces a novel continual learning approach combining orthogonal weights modification and a context-dependent processing module, enabling neural networks to learn multiple context-specific tasks efficiently and without catastrophic forgetting.

Contribution

The paper presents a new learning algorithm and module that allow neural networks to learn numerous context-dependent mappings continually with minimal samples, addressing catastrophic forgetting.

Findings

OWM effectively prevents catastrophic forgetting.

CDP enables reuse of features across contexts.

Networks learn multiple tasks with as few as 10 samples each.

Abstract

Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts. To lift such limits, we developed a novel approach involving a learning algorithm, called orthogonal weights modification (OWM), with the addition of a context-dependent processing (CDP) module. We demonstrated that with OWM to overcome the problem of catastrophic forgetting, and the CDP module to learn how to reuse a feature representation and a classifier for different contexts, a single network can acquire numerous context-dependent mapping rules in an online and continual manner, with as few as $\sim$10 samples to learn each. This should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it.