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Transferable Generative Models.

Jain, Ajay.

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Transferable Generative Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transferable Generative Models./
Author:	Jain, Ajay.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	134 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30493292
ISBN:	9798380877213

Transferable Generative Models.
Jain, Ajay.

Transferable Generative Models. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 134 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2023.

We present progress in developing stable, scalable and transferable generative models for visual data. We first learn expressive image priors using autoregressive models which generate high-quality and diverse images. We then explore transfer learning to generalize visual representations models to new data modalities with limited available data. We propose two methods to generate high quality 3D graphics from sparse input images or natural language descriptions by distilling knowledge from pretrained discriminative vision models. We briefly summarize our work on improving generation quality with a Denoising Diffusion Probabilistic Model, and demonstrate how to transfer it to new modalities including high-quality text-to-3D synthesis using Score Distillation Sampling. Finally, we generate 2D vector graphics from text by optimizing a vector graphics renderer with knowledge distilled from a pretrained text-to-image diffusion model, without vector graphics data. Our models enable high-quality generation across many modalities, and continue to be broadly applied in subsequent work.

ISBN: 9798380877213Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

3D graphics

Transferable Generative Models.
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300 $a 134 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Abbeel, Pieter.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2023.
520 $a We present progress in developing stable, scalable and transferable generative models for visual data. We first learn expressive image priors using autoregressive models which generate high-quality and diverse images. We then explore transfer learning to generalize visual representations models to new data modalities with limited available data. We propose two methods to generate high quality 3D graphics from sparse input images or natural language descriptions by distilling knowledge from pretrained discriminative vision models. We briefly summarize our work on improving generation quality with a Denoising Diffusion Probabilistic Model, and demonstrate how to transfer it to new modalities including high-quality text-to-3D synthesis using Score Distillation Sampling. Finally, we generate 2D vector graphics from text by optimizing a vector graphics renderer with knowledge distilled from a pretrained text-to-image diffusion model, without vector graphics data. Our models enable high-quality generation across many modalities, and continue to be broadly applied in subsequent work.
590 $a School code: 0028.
650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
650 4 $a Information technology. $3 532993
653 $a 3D graphics
653 $a Deep learning
653 $a Text-to-image diffusion model
653 $a Generative AI
690 $a 0984
690 $a 0489
690 $a 0464
710 2 $a University of California, Berkeley. $b Electrical Engineering & Computer Sciences. $3 1671057
773 0 $t Dissertations Abstracts International $g 85-06B.
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792 $a 2023
793 $a English
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