GAN you train your network
| dc.contributor.author | Pamulapati, Venkata Sai Sudeep, author | |
| dc.contributor.author | Blanchard, Nathaniel, advisor | |
| dc.contributor.author | Beveridge, Ross, advisor | |
| dc.contributor.author | King, Emily, committee member | |
| dc.date.accessioned | 2022-08-29T10:15:45Z | |
| dc.date.available | 2022-08-29T10:15:45Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Zero-shot classifiers identify unseen classes — classes not seen during training. Specifically, zero-shot models classify attribute information associated with classes (e.g., a zebra has stripes but a lion does not). Lately, the usage of generative adversarial networks (GAN) for zero-shot learning has significantly improved the recognition accuracy of unseen classes by producing visual features on any class. Here, I investigate how similar visual features obtained from images of a class are to the visual features generated by a GAN. I find that, regardless of metric, both sets of visual features are disjointed. I also fine-tune a ResNet so that it produces visual features that are similar to the visual features generated by a GAN — this is novel because all standard approaches do the opposite: they train the GAN to match the output of the model. I conclude that these experiments emphasize the need to establish a standard input pipeline in zero-shot learning because of the mismatch of generated and real features, as well as the variation in features (and subsequent GAN performance) from different implementations of models such as ResNet-101. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Pamulapati_colostate_0053N_17232.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235560 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | ResNet | |
| dc.subject | generative adversarial networks | |
| dc.subject | zero shot learning | |
| dc.title | GAN you train your network | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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