seminars:stat:nov22023 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>

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^  **DATE:**|Thursday, November 2, 2023 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Geran Zhao, Binghamton University |
^  **TITLE:**|Denoising Diffusion Probabilistic Models  |
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<WRAP centeralign>**Abstract**</WRAP>
The paper presents high quality image synthesis results using
diffusion probabilistic models, a class of latent variable models inspired
by considerations from nonequilibrium thermodynamics. The best results are
obtained in this paper by training on a weighted variational bound designed
according to a novel connection between diffusion probabilistic models and
denoising score matching with Langevin dynamics, and our models naturally
admit a progressive lossy decompression scheme that can be interpreted as a
generalization of autoregressive decoding. On the unconditional CIFAR10
dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID
score of 3.17. On 256x256 LSUN, we obtain sample quality similar to
ProgressiveGAN.

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