상세정보
학술대회
The Present and Future of Continual Learning
- 발행일
- 202010
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1193-1195
- 협약과제
- 20ZR1100, 자율적으로 연결·제어·진화하는 초연결 지능화 기술 연구, 박준희
- 초록
- 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 제안 키워드
- Benchmark datasets, Machine Learning Methods, Multiple tasks, Real-world applications, lifelong learning, state-of-The-Art