Research of Data Augmentation Approaches for Enhancing Classification  Model Performance

dc.contributor.author	Vey, Bohdan
dc.date.accessioned	2024-02-14T08:18:41Z
dc.date.available	2024-02-14T08:18:41Z
dc.date.issued	2023
dc.identifier.citation	Vey, Bohdan. Research of Data Augmentation Approaches for Enhancing Classification Model Performance / Bohdan Vey; Supervisor: Oles Dobosevych; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2023. – 34 p.: ill.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4385
dc.language.iso	en	uk
dc.title	Research of Data Augmentation Approaches for Enhancing Classification Model Performance	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	After a significant improvement in the computational powers of modern comput- ers, the models became larger, and their accuracy increased. However, due to a high amount of parameters, modern neural networks also need much bigger datasets for efficient usage. Augmentation partly solves this problem, but the most up-to- date augmentation still doesn’t change the image patterns. We propose a new way of augmentation by using inpainting models to change the image’s nature. Then we compare model performance by using traditional augmentation and GANAug- mentation. The second part of this study will use Test Time Augmentation(TTA) to improve model performance for data which come from another source.	uk