Inductive Biases in Generative Adversarial Networks

Simple item page
dc.contributor.advisorGrissom, Alvin
dc.contributor.authorLin, Yikang
dc.date.accessioned2023-12-04T17:40:52Z
dc.date.available2023-12-04T17:40:52Z
dc.date.issued2023
dc.description.abstractIn unsupervised learning, generative adversarial network (GAN) is one the generative models for generating new examples that are not present in the original data. Although GANs have impressive results in generating novel examples, the inductive biases of the model can lead to biased results in image generation. This paper provides a summary of two types of GAN-the MLP GAN and StyleGAN-and their corresponding inductive biases. Because analytical analysis of the effects of GAN inductive biases is difficult, an empirical method for studying the inductive biases is also discussed.
dc.description.sponsorshipHaverford College. Department of Computer Science
dc.identifier.urihttp://hdl.handle.net/10066/50045
dc.language.isoeng
dc.rights.accessDark Archive until 2028-01-01, afterwards Haverford users only
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleInductive Biases in Generative Adversarial Networks
dc.typeThesis
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
2023LinY.pdf
Size:
16.58 MB
Format:
Adobe Portable Document Format
Description:
Thesis
Download
Thumbnail Image
Name:
2023LinY_release.pdf
Size:
137.64 KB
Format:
Adobe Portable Document Format
Description:
** Archive Staff Only **
Download
Collections
Computer Science