Efficient Continual Learning in Neural Networks with Embedding Regularization

TL;DR

This paper introduces an efficient continual learning method that regularizes internal embeddings of neural networks, reducing memory and computational costs while maintaining performance, and explores the impact of activation functions on forgetting.

Contribution

It proposes a novel embedding regularization technique combined with a dynamic sampling strategy for scalable continual learning.

Findings

Outperforms state-of-the-art methods in accuracy

Requires less memory and computational time

Analyzes the impact of activation functions on catastrophic forgetting

Abstract

Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures. Previous approaches to the problem have considered either the progressive increase in the size of the networks, or have tried to regularize the network behavior to equalize it with respect to previously observed tasks. In the latter case, it is essential to understand what type of information best represents this past behavior. Common techniques include regularizing the past outputs, gradients, or individual weights. In this work, we propose a new, relatively simple and efficient method to perform continual learning by regularizing instead the network internal embeddings. To make the approach scalable, we also propose a dynamic sampling strategy to reduce the memory footprint of the required external storage. We show that our method performs favorably with respect to state-of-the-art approaches in the literature, while requiring significantly less space in memory and computational time. In addition, inspired inspired by to recent works, we evaluate the impact of selecting a more flexible model for the activation functions inside the network, evaluating the impact of catastrophic forgetting on the activation functions themselves.