Dual Memory

Dual memory systems in machine learning aim to improve model performance and adaptability by incorporating multiple memory components to store and utilize information from past experiences. Current research focuses on developing architectures that effectively combine short-term and long-term memory for tasks like object segmentation, vision-language adaptation, and continual learning, often employing techniques like sparse coding and memory-interactive strategies. These advancements enhance model robustness, reduce catastrophic forgetting, and enable more efficient learning from limited data, impacting fields such as autonomous driving and improving the interpretability and usability of AI models.