2026-09-19T02:43:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1528302023-11-03T03:35:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Guttag, John V.
Durand, Frédo
Lewis, Kathleen M.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-11-02T20:19:33Z
2023-11-02T20:19:33Z
2023-09
2023-09-21T14:26:14.399Z
https://hdl.handle.net/1721.1/152830
Generative AI is a field that is rapidly developing and growing in scale. As research in this area shifts to building on large-scale foundation models and powerful architectures, careful thought has to go into adapting these models to new domains and tasks. The work in this thesis demonstrates novel approaches to adapting large-scale generative models and architectures to specific applications in virtual try-on, conceptual art, and domain-specific image classification. In addition to the technical contributions, this thesis explores broader open questions about domain-specific generative models; for example, how can we carefully construct our training data to mitigate bias? What do human-in-the-loop methods for creative generative AI look like in practice? To what extent are large-scale vision-language models useful for traditionally image-only tasks?
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Developing Domain-Specific Generative Methods
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="806eeebf-c02d-49c4-8f8f-850eea933e75">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Developing Domain-Specific Generative Methods</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2023-09</PublicationDate>
<Authors>
<Author>
<DisplayName>Lewis, Kathleen M.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>Generative AI is a field that is rapidly developing and growing in scale. As research in this area shifts to building on large-scale foundation models and powerful architectures, careful thought has to go into adapting these models to new domains and tasks. The work in this thesis demonstrates novel approaches to adapting large-scale generative models and architectures to specific applications in virtual try-on, conceptual art, and domain-specific image classification. In addition to the technical contributions, this thesis explores broader open questions about domain-specific generative models; for example, how can we carefully construct our training data to mitigate bias? What do human-in-the-loop methods for creative generative AI look like in practice? To what extent are large-scale vision-language models useful for traditionally image-only tasks?</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>