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, practical approaches for the Wyner–Ziv 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 recovers some principles of the optimum theoretical solution. 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.

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