This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

Data Science Case Study

The computing scripts associated with our paper entitled "Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models".

Implementation of augmentation, SMOTE, ADASYN, AE, DGAN for oversampling image data used in detection (YOLO and others one stage detectors). Maritime flags/code scenario.

Streamlining the process to generate synthetic data. Just focus on the data, not the code!