Extreme Model Compression with Structured Sparsity at Low Precision


Dan Liu (McGill University), Nikita Dvornik (Palona AI), Xue Liu (Mohamed bin Zayed University of Artificial Intelligence)
The 35th British Machine Vision Conference
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Abstract

Deep neural networks (DNNs) are used in many applications, but their large size and high computational cost make them hard to run on devices with limited resources. Two widely used techniques to address this challenge are weight quantization, which lowers the precision of all weights, and structured sparsity, which removes unimportant weights while retaining the important ones at full precision. Although both are effective individually, they are typically studied in isolation due to their compounded negative impact on model accuracy when combined. In this work, we introduce SLOPE (Structured sparsity at Low PrEcision), a unified framework that is the first to effectively combine structured sparsity and low-bit quantization in a principled way. We show that naïvely combining sparsity and quantization severely harms performance due to the compounded impact of both techniques. To address this, we propose a training-time regularization strategy that minimizes the discrepancy between full-precision weights and their sparse, quantized counterparts by promoting angular alignment rather than direct matching. On ResNet-18, SLOPE achieves $\sim20\times$ model size reduction while retaining $\sim$99\% of the original accuracy. It consistently outperforms state-of-the-art quantization and structured sparsity methods across classification, detection, and segmentation tasks on models such as ResNet-18, ViT-Small, and Mask R-CNN.

Citation

@inproceedings{Liu_2025_BMVC,
author    = {Dan Liu and Nikita Dvornik and Xue Liu},
title     = {Extreme Model Compression with Structured Sparsity at Low Precision},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_320/paper.pdf}
}


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