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Parallel Multiscale Autoregressive Density Estimation
[article]
2017
arXiv
pre-print
PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density
arXiv:1703.03664v1
fatcat:h3nz2vwrkrgv3na5llqq2f632u