Generative Rehearsal

Generative rehearsal is a continual learning technique aiming to mitigate catastrophic forgetting, where neural networks lose previously learned knowledge when trained on new data. Current research focuses on improving the efficiency and effectiveness of generative models, such as variational autoencoders and normalizing flows, for generating synthetic data representing past tasks, often incorporating techniques like knowledge distillation and latent space manipulation to enhance the quality of generated samples and reduce computational overhead. These advancements are significant because they enable the development of more robust and adaptable AI systems capable of learning continuously from streaming data, with applications ranging from large language models to robotics and 3D point cloud processing.