Continual Learning of Numerous Tasks from Long-tail Distributions

arXiv:2404.02754v1 Announce Type: new
Abstract: Continual learning, an important aspect of artificial intelligence and machine learning research, focuses on developing models that learn and adapt to new tasks while retaining previously acquired knowledge. Existing continual learning algorithms usually involve a small number of tasks with uniform sizes and may not accurately represent real-world learning scenarios. In this paper, we investigate the performance of continual learning algorithms with a large number of tasks drawn from a task distribution that is long-tail …

abstract acquired adapt algorithms artificial artificial intelligence arxiv continual cs.lg intelligence knowledge learn machine machine learning research small tasks type uniform