# Hybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks

Source: [Hybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks](<https://www.sota2.com/research/paper/6a3a13ad206542251d7c77d4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-24T05:00:31.418Z

Authors: Minh-Loi Nguyen, Long-Bao Nguyen, Van-Hieu Huynh, Minh-Triet Tran, Trung-Nghia Le

Published: 2026-06-22T07:11:52.000Z

## Abstract

Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these developments, training and deployment of neural network models on embedding and edge devices face significant challenges due to limited memory and computational resources. These problems can be addressed with deep neural network compression, which involves a trade-off between model size and performance. In this paper, we propose a novel method for model compression through two phases. First, we utilize model compression techniques, such as pruning and quantization, to significantly reduce the model size. Then, we use Mixture of Experts to route the previously compressed models to enhance performance while maintaining a balance in inference efficiency. MoEs consist of multiple expert models (i.e., compressed models) that are moderately sized and deliver stable performance. Experimental results on several benchmark datasets show that our method successfully compresses CNN models which achieves substantial reductions in FLOPs and parameters with a negligible accuracy drop.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-10 (Accuracy, FLOPs, and Inference Speed)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-accuracy-flops-and-inference-speed.md>): Image Classification; CIFAR-10; Accuracy: 92.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.22935>)

[PDF](<https://arxiv.org/pdf/2606.22935.pdf>)