| dc.contributor.advisor | Erik Hemberg. | en_US |
| dc.contributor.author | Woldu, Kifle(Kifle H.) | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2020-09-15T22:03:03Z | |
| dc.date.available | 2020-09-15T22:03:03Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/127547 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020 | en_US |
| dc.description | Cataloged from the official PDF of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 37-38). | en_US |
| dc.description.abstract | Generative Adversarial Networks(GANs) have become very popular for their use in generating high quality images. Unfortunately, GANs also suffer from training instability, making them hard to use in practice[5]. In this thesis, we investigate a specific form of instability called mode collapse, where the model only learns a portion of the distribution. We augment standard GANs with approaches from evolutionary computing and find the augmentation does improve diversity substantially. Additionally, we develop new evolutionary models that further encourage diversity, along with an accompanying modular framework. | en_US |
| dc.description.statementofresponsibility | by Kifle Woldu. | en_US |
| dc.format.extent | 38 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Encouraging GAN diversity via evolutionary computing | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1193031976 | en_US |
| dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2020-09-15T22:03:03Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |
