Gradient Projection Memory for Continual Learning

TL;DR

This paper introduces Gradient Projection Memory (GPM), a novel continual learning method that prevents forgetting by orthogonal gradient updates based on SVD-derived subspaces, achieving competitive results without network growth or data replay.

Contribution

The paper proposes a new continual learning approach using orthogonal gradient steps guided by SVD-based subspace analysis, reducing interference and forgetting.

Findings

GPM effectively mitigates forgetting in continual learning tasks.

GPM achieves comparable or superior performance to state-of-the-art methods.

The approach requires no network growth or data replay.

Abstract

The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propose a novel approach where a neural network learns new tasks by taking gradient steps in the orthogonal direction to the gradient subspaces deemed important for the past tasks. We find the bases of these subspaces by analyzing network representations (activations) after learning each task with Singular Value Decomposition (SVD) in a single shot manner and store them in the memory as Gradient Projection Memory (GPM). With qualitative and quantitative analyses, we show that such orthogonal gradient descent induces minimum to no interference with the past tasks, thereby mitigates forgetting. We evaluate our algorithm on diverse image classification datasets with short and long sequences of tasks and report better or on-par performance compared to the state-of-the-art approaches.