| DC Field | Value | Language |
|---|
| dc.contributor.author | Qing Bu | - |
| dc.contributor.author | Wei Wan | - |
| dc.contributor.author | Leonov, I. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-02-22T06:55:24Z | - |
| dc.date.available | 2024-02-22T06:55:24Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Qing Bu. Hidden Object Masking using Deep Learning / Qing Bu, Wei Wan, I. Leonov // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 320–323. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54317 | - |
| dc.description.abstract | Image inpainting, the process of filling in missing or damaged regions within images, has witnessed a significant evolution in recent years, driven primarily by deep learning methodologies. This paper provides an overview of modern architectures used for image inpainting, and addresses how they can be applied to protect sensitive information. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | image inpainting | en_US |
| dc.subject | WGAN | en_US |
| dc.subject | generative adversarial network | en_US |
| dc.subject | WGAIN | en_US |
| dc.subject | image imputation | en_US |
| dc.title | Hidden Object Masking using Deep Learning | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
|
