The Present and Future of Continual Learning

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 a...

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Bibliographic Details
Published in2020 International Conference on Information and Communication Technology Convergence (ICTC) pp. 1193 - 1195
Main Authors Bae, Heechul, Song, Soonyong, Park, Junhee
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.10.2020
Subjects
catastrophic forgetting
continual lifelong learning
incremental multi-task classification
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Summary: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.
DOI:10.1109/ICTC49870.2020.9289549