Generative Replay

Generative replay is a continual learning technique aiming to mitigate catastrophic forgetting, where a model trained sequentially on different tasks loses its ability to perform well on previously learned tasks. Current research focuses on integrating generative models, such as variational autoencoders and diffusion models, into various continual learning paradigms (e.g., class-incremental learning, federated learning, domain adaptation) to synthesize representative data from past tasks for replay during training. This approach is significant because it enables models to adapt to new data streams while preserving previously acquired knowledge, improving the robustness and scalability of AI systems across diverse and evolving real-world applications.