# DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression

arXiv: https://arxiv.org/abs/1905.08318 · HTML: https://mlsanity.com/paper/1905.08318/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 5 of 5)

- [LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification](https://mlsanity.com/paper/2204.02965/) (2022)
- [Lossy and Lossless (L2) Post-training Model Size Compression](https://mlsanity.com/paper/2308.04269/) (2023)
- [From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks](https://mlsanity.com/paper/2405.06038/) (2024)
- [Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space](https://mlsanity.com/paper/2504.14471/) (2025)
- [EntroPack: Fast and Accurate Entropy-Coded Weight Compression at Arbitrary Bitrates](https://mlsanity.com/paper/2609.34185/) (2026)

## Abstract (arXiv)

We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distortion function, which implicitly takes the impact of quantization on to the accuracy of the network into account. Subsequently, it compresses the quantized values into a bitstream representation with minimal redundancies. We show that DeepCABAC is able to reach very high compression ratios across a wide set of different network architectures and datasets. For instance, we are able to compress by x63.6 the VGG16 ImageNet model with no loss of accuracy, thus being able to represent the entire network with merely 8.7MB.