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DTSTART:20240310T080000
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DTSTART;TZID=America/Chicago:20240219T123000
DTEND;TZID=America/Chicago:20240219T133000
DTSTAMP:20260514T210307
CREATED:20240318T213630Z
LAST-MODIFIED:20240318T213656Z
UID:2897-1708345800-1708349400@ifds.info
SUMMARY:A good score does not lead to a good generative model
DESCRIPTION:Speaker: Sixu Li \nAbstract: Score-based Generative Models (SGMs) is one leading method in generative modeling\, renowned for their ability to generate high-quality samples from complex\, high-dimensional data distributions. The method enjoys empirical success and is supported by rigorous theoretical convergence properties. In particular\, it has been shown that SGMs can generate samples from a distribution that is close to the ground-truth if the underlying score function is learned well\, suggesting the success of SGM as a generative model. We provide a counter-example in this paper. Through the sample complexity argument\, we provide one specific setting where the score function is learned well. Yet\, SGMs in this setting can only output samples that are Gaussian blurring of training data points\, mimicking the effects of kernel density estimation. The finding resonates a series of recent finding that reveal that SGMs can demonstrate strong memorization effect and fail to generate. This is joint with Shi Chen and Qin Li. 
URL:https://ifds.info/event/a-good-score-does-not-lead-to-a-good-generative-model/
LOCATION:WID 1145\, 330 N Orchard Street\, Madison\, WI\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
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