Incremental Learning: Incremental Learning | AI Knowledge Hub

Incremental Learning

Incremental learning and continual learning

Incremental learning (Incremental Learning) allows models to learn from new data, preserving knowledge from previous data, without the need for retraining on the entire dataset. Incremental learning has a wide range of applications: from continual learning and lifelong learning to adaptive systems and personalization. The main challenge of incremental learning is preventing catastrophic forgetting, when a model forgets old knowledge during training on new data. With the development of methods to prevent forgetting and efficient learning, incremental learning has become more practical. Understanding the principles of incremental learning, methods for preventing forgetting, and its applications is critically important for adaptive AI systems.

2. Stability-Plasticity Dilemma

Balance between preserving old knowledge and learning new:

Stability: Preserving existing knowledge.

Generative Replay: Generating Old Examples.

2. Regularization Methods

Regularization for knowledge preservation:

Frequently asked questions