Model Compression
It addresses the computational and hardware challenges of deploying large machine learning models, though it appears incremental in nature.
This paper explores model compression techniques by analyzing combinations of pruning and quantization, proposing a quality metric to optimize the trade-off between accuracy preservation and model size reduction.
With time, machine learning models have increased in their scope, functionality and size. Consequently, the increased functionality and size of such models requires high-end hardware to both train and provide inference after the fact. This paper aims to explore the possibilities within the domain of model compression, discuss the efficiency of combining various levels of pruning and quantization, while proposing a quality measurement metric to objectively decide which combination is best in terms of minimizing the accuracy delta and maximizing the size reduction factor.