articleNature Machine IntelligenceDec 5, 2022HYBRID OA

Three types of incremental learning

GMGido M. van de VenTTTinne TuytelaarsASAndreas S. Tolias

Baylor College of Medicine · University of Cambridge · +2 more institutions

PubMed
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Abstract

Incrementally learning new information from a non-stationary stream of data, referred to as 'continual learning', is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed, but comparing their performances is difficult due to the lack of a common framework. To help address this, we describe three fundamental types, or 'scenarios', of continual learning: task-incremental, domain-incremental and class-incremental learning. Each of these scenarios has its own set of challenges. To illustrate this, we provide a comprehensive empirical comparison of currently used continual learning…

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Keywords
  • Computer science
  • Artificial intelligence
  • MNIST database
  • Machine learning
  • Categorization
  • Deep learning
  • Benchmark (surveying)
  • Multi-task learning
UN Sustainable Development Goals
  • Quality Education
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