Date of Award
4-1-2022
Document Type
Honors Thesis
Department
Engineering & Computer Science
First Advisor
Rodney Summerscales
Second Advisor
Hyun Kwon
Abstract
Machine Learning (ML) is vastly improving the world, from computer vision to fully self-driving cars, we are now able accomplish objectives that were thought to only be dreams. In order to train ML models accurately, they require mountains of information to work with, but sometimes it becomes impossible to collect the data needed, so we turn to data augmentation. In this project we use a conditional variational auto encoder to supplement the original video electrochemiluminescence biosensor dataset, in order to increase the accuracy of a future classification model. In other words, using a cVAE we will create unique realistic videos to combine with the dataset.
Recommended Citation
Dulcich, Matthew, "Conditional Variational Autoencoder (cVAE) for the Augmentation of ECL Biosensor Data" (2022). Honors Theses. 264.
https://digitalcommons.andrews.edu/honors/264
Subject Area
Machine learning; Database management; Computer vision; Conditional Variational Autoencoder; Data augmentation
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
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