@inproceedings{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}