Abstract
Nowadays, the use of transfer learning, a deep learning technique, is growing to solve imaging problems in several contexts such as biomedicine where the amount of images is limited. However, applying transfer learning might be challenging for users without experience due to the complexity of the deep learning frameworks. To facilitate the task of creating and using transfer learning models, we developed FrImCla, a framework for creating image classification models. In this paper, we have developed a set of Jupyter notebooks that use FrImCla to facilitate the task of creating and using image classification models for users without knowledge in deep learning frameworks and without any special purpose hardware.
Partially supported by Ministerio de Industria, Economía y Competitividad, project MTM2017-88804-P; Agencia de Desarrollo Económico de La Rioja, project 2017-I-IDD-00018; and FPI Grant of the Comunidad Autónoma de La Rioja.
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García-Domínguez, M., Domínguez, C., Heras, J., Mata, E., Pascual, V. (2020). Jupyter Notebooks for Simplifying Transfer Learning. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_27
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