Date of Award
Engineering & Computer Science
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.
Dulcich, Matthew, "Conditional Variational Autoencoder (cVAE) for the Augmentation of ECL Biosensor Data" (2022). Honors Theses. 264.
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.
Files over 3MB may be slow to open. For best results, right-click and select "save as..."