Continual Learning

Continual learning, sometimes called lifelong learning, aims to help models acquire new information over time while retaining capabilities learned earlier. Standard training often assumes a fixed dataset, but real applications face changing users, environments, classes, and requirements. Updating a model naively can cause catastrophic forgetting, where performance on older tasks collapses as the system adapts to recent data. Continual-learning methods address this with replayed examples, protected parameters, modular components, regularization, or dynamic architectures. The approach is valuable for robotics, personalization, fraud detection, and other evolving settings, although measuring stability, privacy, data quality, and safe behavior across many updates remains difficult.