# Compression strategies and space-conscious representations for deep neural networks

arXiv: https://arxiv.org/abs/2007.07967 · HTML: https://mlsanity.com/paper/2007.07967/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters](https://mlsanity.com/paper/2112.06563/) (2021)
- [Compact representations of convolutional neural networks via weight pruning and quantization](https://mlsanity.com/paper/2108.12704/) (2021)
- [Resource-Limited Automated Ki67 Index Estimation in Breast Cancer](https://mlsanity.com/paper/2401.00014/) (2023)
- [Quantization-Aware Regularizers for Deep Neural Networks Compression](https://mlsanity.com/paper/2602.03614/) (2026)

## Abstract (arXiv)

Recent advances in deep learning have made available large, powerful convolutional neural networks (CNN) with state-of-the-art performance in several real-world applications. Unfortunately, these large-sized models have millions of parameters, thus they are not deployable on resource-limited platforms (e.g. where RAM is limited). Compression of CNNs thereby becomes a critical problem to achieve memory-efficient and possibly computationally faster model representations. In this paper, we investigate the impact of lossy compression of CNNs by weight pruning and quantization, and lossless weight matrix representations based on source coding. We tested several combinations of these techniques on four benchmark datasets for classification and regression problems, achieving compression rates up to 165 times, while preserving or improving the model performance.