# Learned Wyner-Ziv Compressors Recover Binning

arXiv: https://arxiv.org/abs/2305.04380 · HTML: https://mlsanity.com/paper/2305.04380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2023.

## Built on by (most cited first, 2 of 2)

- [Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information](https://mlsanity.com/paper/2310.04311/) (2023)
- [Learned layered coding for Successive Refinement in the Wyner-Ziv Problem](https://mlsanity.com/paper/2311.03061/) (2023)

## Abstract (arXiv)

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case of distributed source coding. To this day, real-world applications of this problem have neither been fully developed nor heavily investigated. We propose a data-driven method based on machine learning that leverages the universal function approximation capability of artificial neural networks. We find that our neural network-based compression scheme re-discovers some principles of the optimum theoretical solution of the Wyner-Ziv setup, such as binning in the source space as well as linear decoder behavior within each quantization index, for the quadratic-Gaussian case. These behaviors emerge although no structure exploiting knowledge of the source distributions was imposed. Binning is a widely used tool in information theoretic proofs and methods, and to our knowledge, this is the first time it has been explicitly observed to emerge from data-driven learning.