RePLAy Loss

"Replay," in machine learning, refers to techniques that reuse past experiences (data or model states) to improve learning efficiency and mitigate catastrophic forgetting in continual learning scenarios. Current research focuses on optimizing replay buffer construction and sample selection strategies, often incorporating elements like prioritized experience replay, curiosity-driven selection, and asymmetric sampling within various model architectures, including recurrent neural networks and generative models. These advancements are significant for improving the robustness and data efficiency of AI systems across diverse applications, such as recommendation systems, object detection, and reinforcement learning.