DAugtify: Revolutionizing Computer Vision Performance with Automated Data Augmentation

Abstract

"The performance of modern Convolutional Neural Networks (CNN) heavily depends on the quality and the volume of the dataset. However, collecting proper training datasets in many real-world domains is well-known to be labor-intensive and expensive. Data augmentation (DA) is a widely accepted solution to improve the low diversity datasets. But selecting optimal DA technique combinations (DA policies) based on the given dataset using traditional trial-and-error approach is time-consuming and requires domain expertise. In this study, the author attempts to automate the process of selecting optimal DA policies based on the given dataset by further enhancing Automated Data Augmentation (AutoDA) technologies. To achieve this goal, the author redefined the traditional AutoDA search space by reducing the number of hyper-parameters and proposing novel search space exploration strategy that utilizes neural network and gradient decent technologies. Through these modifications, the author was able to bridge the existing research gap of achieving efficiency and effectiveness of AutoDA solutions at the same time."