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dc.contributor.author | Vey, Bohdan![]() |
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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 |