Rehearsal methods (also called replay methods) are continual learning techniques that combat catastrophic forgetting by storing and periodically replaying examples from previously learned tasks while training on new tasks. They are among the most effective approaches to continual learning.

Core Idea

• Maintain a memory buffer containing representative examples from past tasks.
• When training on a new task, interleave new task data with replayed examples from the buffer.
• This ensures the model continues to see old data, preventing weights from drifting away from solutions that work for previous tasks.

Types of Rehearsal

• Exact Replay: Store actual training examples from previous tasks. Simple and effective but requires memory for storing raw data.
• Generative Replay: Train a generative model (GAN, VAE) on previous task data and use it to generate synthetic examples for replay. No need to store real data, but the quality of generated examples matters.
• Feature Replay: Store intermediate feature representations rather than raw inputs. More compact than raw data storage.
• Gradient-Based Replay: Store gradient information from previous tasks and use it to constrain learning on new tasks (e.g., GEM — Gradient Episodic Memory).

Key Design Decisions

• Buffer Size: How many examples to store. Larger buffers preserve more information but consume more memory.
• Example Selection: Which examples to keep in the buffer (see exemplar selection strategies).
• Replay Ratio: How often to replay old examples relative to new data. Too little replay → forgetting; too much → slow learning on new tasks.
• Buffer Update: When to add new examples and which old examples to evict as the buffer fills.

Effectiveness

• Rehearsal methods consistently outperform regularization-only approaches (like EWC) on standard continual learning benchmarks.
• Even a very small buffer (50–100 examples per class) provides significant forgetting prevention.
• Combining rehearsal with regularization further improves results.

Limitations

• Privacy: Storing real examples from previous tasks may violate privacy constraints.
• Scalability: Buffer size grows with the number of tasks (or examples must be evicted).

Rehearsal methods are the most practical and effective approach to continual learning in production systems — simple exact replay with a well-designed buffer is hard to beat.