Model compression is a machine learning technique for reducing the size of trained models. Large models can achieve high accuracy, but often at the cost of significant resource requirements.

Sep 2, 2024 · Comprehensive review of model compression techniques: we provide an in-depth review of various model compression strategies, including pruning, quantization, low-rank factorization, …

In this article, I will go through four fundamental compression techniques that every ML practitioner should understand and master. I explore pruning, quantization, low-rank factorization, and …

We present a method for model compression that is able to train fast, compact models to mimic better performing, but slow and complex models with little loss in performance.

Model compression refers to a category of methods used in deep learning to reduce the storage and energy consumption of models by sparsifying neural network parameters, especially for on-device …

To address this limitation, techniques and methodologies for model compression have been attempted to reduce the storage requirement of deep neural networks without impacting the original accuracy. …

Apr 2, 2025 · Model compression techniques aim to reduce the size and computational requirements of neural networks while maintaining their accuracy. This enables faster inference, lower power …

Mar 20, 2025 · As these challenges intensify, model compression has become a vital research focus to address these limitations. This paper presents a comprehensive review of the evolution of model …

Nov 18, 2025 · Learn essential model compression techniques for 2025. Our guide covers pruning, quantization, and knowledge distillation to create smaller, faster AI models. Read now!

Jan 19, 2022 · Model compression reduces the size of a neural network (NN) without compromising accuracy. This size reduction is important because bigger NNs are difficult to deploy on resource …