Szczegóły publikacji
Opis bibliograficzny
Transfer learning methods as a new approach in computer vision tasks with small datasets / Andrzej BRODZICKI, Michał PIEKARSKI, Dariusz KUCHARSKI, Joanna JAWOREK-KORJAKOWSKA, Marek GORGOŃ // Foundations of Computing and Decision Sciences ; ISSN 0867-6356. — 2020 — vol. 45 no. 3, s. 179-193. — Bibliogr. s. 191-193, Abstr. — Publikacja dostępna online od: 2020-09-18. — M. Piekarski - dod. afiliacja: SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Krakow, Poland
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130238 |
|---|---|
| Data dodania do BaDAP | 2020-09-28 |
| Tekst źródłowy | URL |
| DOI | 10.2478/fcds-2020-0010 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Foundations of Computing and Decision Sciences |
Abstract
Deep learning methods, used in machine vision challenges, often face the problem of the amount and quality of data. To address this issue, we investigate the transfer learning method. In this study, we briefly describe the idea and introduce two main strategies of transfer learning. We also present the widely-used neural network models, that in recent years performed best in ImageNet classification challenges. Furthermore, we shortly describe three different experiments from computer vision field, that confirm the developed algorithms ability to classify images with overall accuracy 87.2-95%. Achieved numbers are state-of-the-art results in melanoma thickness prediction, anomaly detection and Clostridium di cile cytotoxicity classification problems.