  This paper introduces Associative Compression Networks (ACNs), a new
framework for variational autoencoding with neural networks. The system differs
from existing variational autoencoders (VAEs) in that the prior distribution
used to model each code is conditioned on a similar code from the dataset. In
compression terms this equates to sequentially transmitting the dataset using
an ordering determined by proximity in latent space. Since the prior need only
account for local, rather than global variations in the latent space, the
coding cost is greatly reduced, leading to rich, informative codes. Crucially,
the codes remain informative when powerful, autoregressive decoders are used,
which we argue is fundamentally difficult with normal VAEs. Experimental
results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover
high-level latent features such as object class, writing style, pose and facial
expression, which can be used to cluster and classify the data, as well as to
generate diverse and convincing samples. We conclude that ACNs are a promising
new direction for representation learning: one that steps away from IID
modelling, and towards learning a structured description of the dataset as a
whole.
