Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks

TL;DR

This paper introduces a method to decompose neural network activation spaces into task-relevant and irrelevant subspaces, revealing how changes affect stability and plasticity in continual learning.

Contribution

It proposes a novel subspace decomposition technique to distinguish changes impacting prior tasks from those that do not, enhancing understanding of stability-plasticity trade-offs.

Findings

Not all activation changes cause forgetting.

Regularisation techniques do not fully separate task-relevant and irrelevant subspaces.

Manipulating subspaces in a linear model shows causal links to stability and plasticity.

Abstract

Continual learning algorithms strive to acquire new knowledge while preserving prior information. Often, these algorithms emphasise stability and restrict network updates upon learning new tasks. In many cases, such restrictions come at a cost to the model's plasticity, i.e. the model's ability to adapt to the requirements of a new task. But is all change detrimental? Here, we approach this question by proposing that activation spaces in neural networks can be decomposed into two subspaces: a readout range in which change affects prior tasks and a null space in which change does not alter prior performance. Based on experiments with this novel technique, we show that, indeed, not all activation change is associated with forgetting. Instead, only change in the subspace visible to the readout of a task can lead to decreased stability, while restricting change outside of this subspace is associated only with a loss of plasticity. Analysing various commonly used algorithms, we show that regularisation-based techniques do not fully disentangle the two spaces and, as a result, restrict plasticity more than need be. We expand our results by investigating a linear model in which we can manipulate learning in the two subspaces directly and thus causally link activation changes to stability and plasticity. For hierarchical, nonlinear cases, we present an approximation that enables us to estimate functionally relevant subspaces at every layer of a deep nonlinear network, corroborating our previous insights. Together, this work provides novel means to derive insights into the mechanisms behind stability and plasticity in continual learning and may serve as a diagnostic tool to guide developments of future continual learning algorithms that stabilise inference while allowing maximal space for learning.