Slimmable Network

Slimmable networks are a class of neural networks designed to efficiently produce models of varying sizes and computational costs from a single, larger "super-network." Research focuses on adapting this architecture to diverse applications, including autonomous navigation, keyword spotting, and self-supervised learning, often employing convolutional neural networks and transformers. This approach offers significant advantages by reducing the need for training multiple models individually, thereby saving time and resources while maintaining comparable or even superior performance across different hardware constraints. The resulting efficiency improvements are particularly impactful for resource-limited devices and applications.