Dynamic Deep

Dynamic deep learning focuses on creating neural network architectures and optimization techniques that adapt their structure or behavior during execution, improving efficiency and performance. Current research emphasizes developing dynamic models for various tasks, including super-resolution, graph classification, and survival prediction, often employing techniques like conditional variational autoencoders and novel layer designs incorporating rule-based learning. These advancements aim to address limitations of static DNNs, such as catastrophic forgetting in incremental learning and inefficient resource utilization in edge computing, leading to more efficient and adaptable AI systems across diverse applications.