  Distributed source coding (DSC) is the task of encoding an input in the
absence of correlated side information that is only available to the decoder.
Remarkably, Slepian and Wolf showed in 1973 that an encoder without access to
the side information can asymptotically achieve the same compression rate as
when the side information is available to it. While there is vast prior work on
this topic, practical DSC has been limited to synthetic datasets and specific
correlation structures. Here we present a framework for lossy DSC that is
agnostic to the correlation structure and can scale to high dimensions. Rather
than relying on hand-crafted source modeling, our method utilizes a conditional
Vector-Quantized Variational Autoencoder (VQ-VAE) to learn the distributed
encoder and decoder. We evaluate our method on multiple datasets and show that
our method can handle complex correlations and achieves state-of-the-art PSNR.
Our code is made available at https://github.com/acnagle/neural-dsc.
