P-04 Conditional Variational Auto Encoder (cVAE) for Augmentation of ECL Biosensor Data

Buller Hall, Student Lounge

Machine Learning (ML) is vastly improving the world, from computer vision to fully self-driving cars, we are now able to 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 (cVAE) 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.