Training
Continual Learning
Quick Answer
Training models on sequences of tasks without forgetting previously learned knowledge.
Continual learning addresses learning new tasks without forgetting old ones. Without care, training on new tasks (catastrophic forgetting) degrades old task performance. Continual learning techniques include replay, regularization, and architecture design. Maintaining flexibility while retaining knowledge is challenging. Continual learning is important for models adapting over time. This contrasts with single-shot training on fixed datasets. Practical continual learning is an active research area.
Last verified: 2026-04-08