Stores a subset of past training samples in a memory buffer and replays them alongside new task data during training. This interleaving of old and new examples helps the model retain previously learned representations while adapting to new tasks, effectively combating catastrophic forgetting through data rehearsal.
2017
Uses a generative model (e.g., GAN or VAE) to synthesize pseudo-samples from previous tasks instead of storing raw data. The generator learns the distribution of past tasks, enabling unlimited replay without requiring a memory buffer, making it particularly suitable for privacy-sensitive applications.
2017
Stores a small episodic memory of past examples and uses them to constrain gradient updates during training on new tasks. GEM projects the gradient onto a feasible region that does not increase the loss on previous tasks, providing formal guarantees against forgetting.
2019
An efficient variant of GEM that replaces the per-task gradient constraints with a single averaged constraint computed from a random subset of the episodic memory. This dramatically reduces computational overhead while maintaining comparable performance to GEM.