Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/73325

Facilitating Visual to Parametric Interaction with Deep Contrastive Learning

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Item Summary

Title:Facilitating Visual to Parametric Interaction with Deep Contrastive Learning
Authors:Wooton, Billy Troy
Contributors:Leigh, Jason (advisor)
Computer Science (department)
Keywords:Computer science
Contrastive Learning
Deep Learning
Human in the loop machine learning
Visual Analytics
show 1 moreVisual to Parametric Interaction
show less
Date Issued:2020
Publisher:University of Hawai'i at Manoa
Abstract:This thesis presents an approach to facilitating Visual to Parametric Interactions (V2PIs) by leveraging deep contrastive learning models. Within the larger contexts of Human-in-the-loop Machine Learning and interactive visual analytics, V2PI systems aim to empower domain scientists and analysts to manipulate the internal parameters of a parametric projection algorithm via intuitive interactions with a 2D visualization of data. This visualization is generated by the projection algorithm, whose internal parameters dictate how highly dimensional input data are projected down to two-dimensions. In a V2PI system, the domain expert then interacts with this visualization directly, re-positioning points within the 2D projection space based on their domain knowledge and intuition, with the goal of not only exploring alternative projections, but also teaching the parametric algorithm to extract their domain knowledge, and apply it to new out-of-sample data points. In recent years, deep contrastive learning models have risen as a powerful way to learn desirable projections of high-dimensional data, such that data points similar to one another are tightly clustered within the 2D projection, while dissimilar points are spread apart. This thesis explores deep contrastive learning as a compelling candidate for use as the parametric projection algorithm within Visual to Parametric Interaction systems.
Pages/Duration:106 pages
URI:http://hdl.handle.net/10125/73325
Rights:All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: M.S. - Computer Science


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