title: GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING
abstract: The unconditional generation of high fidelity images is a longstanding benchmark
  for testing the performance of image decoders. Autoregressive image models
  have been able to generate small images unconditionally, but the extension of
  these methods to large images where fidelity can be more readily assessed has
  remained an open problem. Among the major challenges are the capacity to encode
  the vast previous context and the sheer difficulty of learning a distribution that
  preserves both global semantic coherence and exactness of detail. To address the
  former challenge, we propose the Subscale Pixel Network (SPN), a conditional
  decoder architecture that generates an image as a sequence of image slices of equal
  size. The SPN compactly captures image-wide spatial dependencies and requires a
  fraction of the memory and the computation. To address the latter challenge, we
  propose to use multidimensional upscaling to grow an image in both size and depth
  via intermediate stages corresponding to distinct SPNs. We evaluate SPNs on the
  unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32
  to 128. We achieve state-of-the-art likelihood results in multiple settings, set up
  new benchmark results in previously unexplored settings and are able to generate
  very high fidelity large scale samples on the basis of both datasets.
authors: Jacob Menick Nal Kalchbrenner
hIndex: Nal Kalchbrenner 25