Memory Based

Memory-based methods are transforming various machine learning domains by enhancing model capabilities to learn and retain information over time, addressing challenges like catastrophic forgetting in continual learning and improving efficiency in tasks involving long sequences. Current research focuses on developing efficient memory architectures, including those leveraging sparse memory, differentiable caches, and uncertainty-aware mechanisms for selective memory updates, often integrated with transformer networks or other deep learning models. These advancements are improving performance in diverse applications such as video anomaly detection, multi-object tracking, and semantic segmentation, while also contributing to a deeper understanding of how memory mechanisms can enhance learning and generalization in artificial systems.