Rehearsal Based Video

Rehearsal-based learning focuses on mitigating catastrophic forgetting in machine learning models, where learning new tasks causes the model to lose previously acquired knowledge. Current research emphasizes efficient sample selection strategies for rehearsal, exploring methods to prioritize the most informative past data points for replay during training, often within the context of continual learning and across various model architectures, including vision transformers and memory networks. This approach is significant for improving the robustness and adaptability of AI systems across diverse and evolving datasets, with applications ranging from video understanding and question answering to conflict resolution training simulations.