Expandable Network

Expandable networks are a class of machine learning models designed to efficiently learn new tasks without forgetting previously acquired knowledge, a crucial challenge in continual learning. Current research focuses on developing resource-efficient architectures, such as dynamically merging or expanding networks with lightweight modules, and employing techniques like knowledge distillation and optimized sample replay to minimize computational overhead and storage requirements. These advancements are significant for applications with limited resources, such as medical image analysis and resource-constrained edge devices, and improve the scalability and adaptability of AI systems.