TzK Flow - Conditional Generative Model
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We introduce TzK (pronounced "task"), a conditional flow-based encoder/decoder generative model, formulated in terms of maximum likelihood (ML). TzK offers efficient approximation of arbitrary data sample distributions (similar to GAN and flow-based ML), and stable training (similar to VAE and ML), while avoiding variational approximations (similar to ML). TzK exploits meta-data to facilitate a bottleneck, similar to autoencoders, thereby producing a low-dimensional representation. Unlike autoencoders, our bottleneck does not limit model expressiveness, similar to flow-based ML. Supervised, unsupervised, and semi-supervised learning are supported by replacing missing observations with samples from learned priors. We demonstrate TzK by jointly training on MNIST and Omniglot with minimal preprocessing, and weak supervision, with results which are comparable to state-of-the-art.
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