Compact Neural Network

Compact neural networks aim to minimize model size and computational cost while maintaining high performance, crucial for deploying AI on resource-constrained devices. Research currently emphasizes efficient training strategies, including specialized optimization techniques and knowledge distillation, alongside the development of novel architectures like GhostNet and the exploration of hardware-aware compression methods such as low-rank approximations and activation function pruning. These advancements are significant for expanding the accessibility and applicability of AI across various domains, from mobile applications to embedded systems and resource-limited environments.