Analysis of Criteria for the Selection of Machine Learning Frameworks

  • With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.

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Metadaten
Author:Kai Dinghofer, Frank HartungORCiD
DOI:https://doi.org/10.1109/ICNC47757.2020.9049650
Parent Title (English):2020 International Conference on Computing, Networking and Communications (ICNC)
Publisher:IEEE
Place of publication:New York, NY
Document Type:Conference Proceeding
Language:English
Year of Completion:2020
Date of the Publication (Server):2020/04/03
First Page:373
Last Page:377
Note:
2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA
Link:https://doi.org/10.1109/ICNC47757.2020.9049650
Zugriffsart:campus
Institutes:FH Aachen / ECSM European Center for Sustainable Mobility
FH Aachen / Fachbereich Elektrotechnik und Informationstechnik
collections:Verlag / IEEE