# DeepZip: Lossless Data Compression using Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1811.08162 · HTML: https://mlsanity.com/paper/1811.08162/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 30 of 33)

- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [Image and Video Tokenization with Binary Spherical Quantization](https://mlsanity.com/paper/2406.07548/) (2024)
- [LLMZip: Lossless Text Compression using Large Language Models](https://mlsanity.com/paper/2306.04050/) (2023)
- [FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression](https://mlsanity.com/paper/2409.17141/) (2024)
- [Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data](https://mlsanity.com/paper/2410.05078/) (2024)
- [Text summarization via global structure awareness](https://mlsanity.com/paper/2602.09821/) (2026)
- [Partition and Code: learning how to compress graphs](https://mlsanity.com/paper/2107.01952/) (2021)
- [In-context learning and Occam's razor](https://mlsanity.com/paper/2410.14086/) (2024)
- [Approximating Human-Like Few-shot Learning with GPT-based Compression](https://mlsanity.com/paper/2308.06942/) (2023)
- [AlphaZip: Neural Network-Enhanced Lossless Text Compression](https://mlsanity.com/paper/2409.15046/) (2024)
- [Few-Shot Non-Parametric Learning with Deep Latent Variable Model](https://mlsanity.com/paper/2206.11573/) (2022)
- [Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction](https://mlsanity.com/paper/2505.06297/) (2025)
- [Training Foundation Models as Data Compression: On Information, Model Weights and Copyright Law](https://mlsanity.com/paper/2407.13493/) (2024)
- [Test-Time Steering for Lossless Text Compression via Weighted Product of Experts](https://mlsanity.com/paper/2511.10660/) (2025)
- [Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs](https://mlsanity.com/paper/2305.09705/) (2023)
- [Ranking LLMs by compression](https://mlsanity.com/paper/2406.14171/) (2024)
- [Tiny Transformers Excel at Sentence Compression](https://mlsanity.com/paper/2410.23510/) (2024)
- [An Enhanced Text Compression Approach Using Transformer-based Language Models](https://mlsanity.com/paper/2412.15250/) (2024)
- [Learned Lossless Compression for JPEG via Frequency-Domain Prediction](https://mlsanity.com/paper/2303.02666/) (2023)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent](https://mlsanity.com/paper/2601.13559/) (2026)
- [LLM-based Source Code Compression via Thresholded Symbol Ranking](https://mlsanity.com/paper/2607.24192/) (2026)
- [OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression](https://mlsanity.com/paper/2111.01662/) (2021)
- [Measuring Information Distortion in Hierarchical Ultra long Novel Reconstruction:The Optimal Expansion Ratio](https://mlsanity.com/paper/2505.12572/) (2025)
- [Learned Static Function Data Structures](https://mlsanity.com/paper/2510.27588/) (2025)
- [AI/ML based Joint Source and Channel Coding for HARQ-ACK Payload](https://mlsanity.com/paper/2511.19943/) (2025)
- [A Model-Driven Lossless Compression Algorithm Resistant to Mismatch](https://mlsanity.com/paper/2601.17684/) (2026)
- [Seq2Seq2Seq: Lossless Data Compression via Discrete Latent Transformers and Reinforcement Learning](https://mlsanity.com/paper/2602.12146/) (2026)
- [Efficient Learned Data Compression via Dual-Stream Feature Decoupling](https://mlsanity.com/paper/2604.07239/) (2026)
- [StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing](https://mlsanity.com/paper/2605.02904/) (2026)

## Abstract (arXiv)

Sequential data is being generated at an unprecedented pace in various forms, including text and genomic data. This creates the need for efficient compression mechanisms to enable better storage, transmission and processing of such data. To solve this problem, many of the existing compressors attempt to learn models for the data and perform prediction-based compression. Since neural networks are known as universal function approximators with the capability to learn arbitrarily complex mappings, and in practice show excellent performance in prediction tasks, we explore and devise methods to compress sequential data using neural network predictors. We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets. The proposed compressor outperforms Gzip on the real datasets and achieves near-optimal compression for the synthetic datasets. The results also help understand why and where neural networks are good alternatives for traditional finite context models