The Present and Future of Continual Learning

Abstract

This paper addresses a continual lifelong learning problem that learns incremental multiple tasks in real-world environments. We overview and summarize representative approaches and categorization of the state-of-the-art in continual learning. Comparable scenarios, benchmark datasets, and baseline approaches for different continual scenarios introduced in this paper. We suggested a comparison of the differences and similarities with other machine learning methods. We also report real-world applications, especially robots and healthcare fields. We summarize current states and suggest future direction of continual learning problems.