PixelPyramids: Exact Inference Models from Lossless Image Pyramids

• URL: http://arxiv.org/abs/2110.08787v1
• Date: Sun, 17 Oct 2021 10:47:29 GMT
• Title: PixelPyramids: Exact Inference Models from Lossless Image Pyramids
• Authors: Shweta Mahajan, Stefan Roth
• Abstract summary: Pixel-Pyramids is a block-autoregressive approach with scale-specific representations to encode the joint distribution of image pixels. It yields state-of-the-art results for density estimation on various image datasets, especially for high-resolution data. For CelebA-HQ 1024 x 1024, we observe that the density estimates are improved to 44% of the baseline despite sampling speeds superior even to easily parallelizable flow-based models.
• Score: 58.949070311990916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autoregressive models are a class of exact inference approaches with highly flexible functional forms, yielding state-of-the-art density estimates for natural images. Yet, the sequential ordering on the dimensions makes these models computationally expensive and limits their applicability to low-resolution imagery. In this work, we propose Pixel-Pyramids, a block-autoregressive approach employing a lossless pyramid decomposition with scale-specific representations to encode the joint distribution of image pixels. Crucially, it affords a sparser dependency structure compared to fully autoregressive approaches. Our PixelPyramids yield state-of-the-art results for density estimation on various image datasets, especially for high-resolution data. For CelebA-HQ 1024 x 1024, we observe that the density estimates (in terms of bits/dim) are improved to ~44% of the baseline despite sampling speeds superior even to easily parallelizable flow-based models.

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