Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets

• Autorzy: Brodzicki Andrzej , Piekarski Michał , Kucharski Dariusz , Jaworek-Korjakowska Joanna , Gorgon Marek

Identyfikatory

DOI

10.2478/fcds-2020-0010

• Języki publikacji: EN

Abstrakty

EN

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 thick- ness prediction, anomaly detection and Clostridium difficile cytotoxicity classification problems

Słowa kluczowe

EN

deep neural networks; transfer learning; signal processing; image analysis; anomaly detection

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2020
• Tom: Vol. 45, No. 3
• Strony: 179--193
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Brodzicki Andrzej

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland

autor

Piekarski Michał

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland
• SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Krakow, Poland

autor

Kucharski Dariusz

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland

autor

Jaworek-Korjakowska Joanna

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland

autor

Gorgon Marek

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).