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MixNMatch: Multifactor Disentangleme...

Li, Yuheng.

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MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation./
作者:	Li, Yuheng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	25 p.
附註:	Source: Masters Abstracts International, Volume: 82-04.
Contained By:	Masters Abstracts International82-04.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27995737
ISBN:	9798672163635

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation.
Li, Yuheng.

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 25 p.

Source: Masters Abstracts International, Volume: 82-04.

Thesis (M.S.)--University of California, Davis, 2020.

This item must not be sold to any third party vendors.

We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders. MixNMatch requires bounding boxes during training to model background, but requires no other supervision. Through extensive experiments, we demonstrate MixNMatch's ability to accurately disentangle, encode, and combine multiple factors for mix-and-match image generation, including sketch2color, cartoon2img, and img2gif applications.Our code/models/demo can be found at https://github.com/Yuheng-Li/MixNMatch.

ISBN: 9798672163635Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Disentanglement

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation.
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506 $a This item must not be sold to any third party vendors.
520 $a We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders. MixNMatch requires bounding boxes during training to model background, but requires no other supervision. Through extensive experiments, we demonstrate MixNMatch's ability to accurately disentangle, encode, and combine multiple factors for mix-and-match image generation, including sketch2color, cartoon2img, and img2gif applications.Our code/models/demo can be found at https://github.com/Yuheng-Li/MixNMatch.
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653 $a Encoding
653 $a Image generation
653 $a Image codes
653 $a sketch2color
653 $a cartoon2img
653 $a img2gif
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690 $a 0389
690 $a 0710
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