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 sub-images of equal size. The SPN compactly captures
image-wide spatial dependencies and requires a fraction of the memory and the
computation required by other fully autoregressive models. To address the
latter challenge, we propose to use Multidimensional Upscaling to grow an image
in both size and depth via intermediate stages utilising distinct SPNs. We
evaluate SPNs on the unconditional generation of CelebAHQ of size 256 and of
ImageNet from size 32 to 256. 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.

本论文提出了一种基于 Subscale Pixel Network 和 Multidimensional Upscaling 的方法，可用于生成高保真度的大规模图像，并在 CelebAHQ 和 ImageNet 的数据集上取得了最先进的结果。