Károly, Artúr István and Tirczka, Sebestyén and Gao, Huijun and Rudas, Imre J. and Galambos, Péter (2023) Increasing the Robustness of Deep Learning Models for Object Segmentation: A Framework for Blending Automatically Annotated Real and Synthetic Data. IEEE TRANSACTIONS ON CYBERNETICS. pp. 1-14. ISSN 2168-2267 (print); 2168-2275 (online)
|
Text
05_CYB23-DL.pdf Available under License Creative Commons Attribution. Download (5MB) | Preview |
Official URL: https://doi.org/10.1109/TCYB.2023.3276485
| Item Type: | Article |
|---|---|
| Subjects: | Q Science / természettudomány > QA Mathematics / matematika > QA76.76 Software Design and Development / Szoftvertervezés és -fejlesztés |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 26 Sep 2023 08:58 |
| Last Modified: | 26 Sep 2023 08:58 |
| URI: | http://real.mtak.hu/id/eprint/174941 |
Actions (login required)
 |
Edit Item |
