Model Compression

Model compression is an active research area aiming to deploy state-of-the-art deep networks on low-power and resource-constrained devices without significantly sacrificing accuracy. By employing techniques such as parameter pruning, low-rank factorization, and weight quantization, the size of deep networks can be effectively reduced, enhancing their applicability in edge computing and mobile devices.