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Analysis of Artifact Formation and Removal in GAN Training

Abstract Details

2023, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Generative Adversarial Networks (GANs) are canonically characterized as a generative model for unsupervised learning where two neural networks compete against each other with the loss of one agent being the gain of the other. Undesired artifact formation is a common problem that occurs during the training process of high-resolution, image generating GANs. These artifacts can appear in several forms, such as checkerboard-like patterns or blurry features that disrupt training and prevent optimal image generation. This work will look at how these artifacts arise and explore several strategies that attempt to mitigate their formation during the training of a DCGAN.
Badri Vellambi Ravisankar, Ph.D. (Committee Chair)
Raj Bhatnagar, Ph.D. (Committee Member)
Jillian Aurisano, Ph.D. (Committee Member)
66 p.

Recommended Citations

Citations

  • Hackney, D. (2023). Analysis of Artifact Formation and Removal in GAN Training [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684771640269023

    APA Style (7th edition)

  • Hackney, Daniel. Analysis of Artifact Formation and Removal in GAN Training. 2023. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684771640269023.

    MLA Style (8th edition)

  • Hackney, Daniel. "Analysis of Artifact Formation and Removal in GAN Training." Master's thesis, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684771640269023

    Chicago Manual of Style (17th edition)