Replay Based Continual Learning

Replay-based continual learning aims to enable machine learning models to learn new tasks sequentially without forgetting previously acquired knowledge, a crucial challenge in many real-world applications. Current research focuses on improving the efficiency and effectiveness of replay buffers, exploring optimal sampling strategies (e.g., prioritizing "mid-learned" examples or using gradient-based selection), and integrating replay with other techniques like model distillation or contrastive learning. These advancements are significant because they address the catastrophic forgetting problem, enabling more robust and adaptable AI systems for diverse applications such as medical image analysis, natural language processing, and predictive modeling in dynamic environments.