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Conference Paper
The Present and Future of Continual Learning
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- Authors
- Heechul Bae, Soonyong Song, Junhee Park
- Issue Date
- 2020-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1193-1195
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- 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.
- KSP Keywords
- Benchmark datasets, Machine Learning Methods, Multiple tasks, Real-world applications, lifelong learning, state-of-The-Art