Date of Award


Document Type

Honors Thesis


Engineering & Computer Science

First Advisor

Rodney Summerscales

Second Advisor

Hyun Kwon


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.

Subject Area

Machine learning; Database management; Computer vision; Conditional Variational Autoencoder; Data augmentation

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