arXiv:1611.05013cs.LG
PixelVAE: A Latent Variable Model for Natural Images
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Abstract
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
I think an aspect of deep learning that is often overlooked is that it is still not clear how much of current algorithm performance is defined by local "obsession to detail" vs global "understanding" of the subject matter. The "tiramisu" layers here are an interesting example of…
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