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Deep Domain Fusion for Adaptive Image Classification


Abstract Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to over... (more)
Created Date 2019
Contributor Dudley, Andrew (Author) / Panchanathan, Sethuraman (Advisor) / Venkateswara, Hemanth (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Subject Computer science / Machine Learning / Semi-Supervised Learning / Unsupervised Domain Adaptation
Type Masters Thesis
Extent 55 pages
Language English
Copyright
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis