Publication year
2021Publisher
[S.l.] : OpenReview.net
In
NeurIPS 2021: DLDE Workshop: The symbiosis of deep learning and differential equations, pp. 1-19Related links
Annotation
35th Conference on Neural Information Processing Systems (NeurIPS 2021) (Sydney, Australia, Dec 13 2021)
Publication type
Article in monograph or in proceedings

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Organization
SW OZ DCC AI
Languages used
English (eng)
Book title
NeurIPS 2021: DLDE Workshop: The symbiosis of deep learning and differential equations
Page start
p. 1
Page end
p. 19
Subject
Cognitive artificial intelligenceAbstract
Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data to noise and generate data by reversing it. Unfortunately, current score-based models generate data very slowly due to the sheer number of score network evaluations required by numerical SDE solvers. In this work, we aim to accelerate this process by devising a more efficient SDE solver. Our solver requires only two score function evaluations per step, rarely rejects samples, and leads to high-quality samples. Our approach generates data 2 to 10 times faster than EM while achieving better or equal sample quality. For high-resolution images, our method leads to significantly higher quality samples than all other methods tested. Our SDE solver has the benefit of requiring no step size tuning.
This item appears in the following Collection(s)
- Academic publications [227900]
- Faculty of Social Sciences [28471]
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