Continual Learning Through the Lens of Adaptive Filtering: A mathematical tutorial

Abstract

Continual learning refers to the problem of learning multiple tasks presented sequentially to the learner without forgetting previously learned tasks. Recently, many deep learning-based approaches have been proposed for continual learning; however, the mathematical foundations behind existing continual learning methods remain underdeveloped. On the other hand, adaptive filtering is a classic subject in signal processing with a rich history of mathematically principled methods. However, its role in understanding the foundations of continual learning has been underappreciated. In this tutorial, we review the basic principles behind both continual learning and adaptive filtering and present a comparative analysis that highlights multiple connections between them. These connections allow us to enhance the mathematical foundations of continual learning based on existing results for adaptive filtering, extend adaptive filtering insights using existing continual learning methods, and discuss a few research directions for continual learning suggested by the historical developments in adaptive filtering.