@misc{15153,
  keywords = {Transfer Learning, CNN, convolutional neural networks, Bayesian optimization, hyperparameter optimization, automatic hyperparameter optimization, saga, keras, tensorflow, gpyopt, dataset manipulation},
  author = {Rune Borgli and Hanna Borgli and P{\r a}l Halvorsen and Michael Riegler and H{\r a}kon Stensland},
  title = {Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task},
  abstract = {This paper details the approach to the MediaEval 2018 Medico Multimedia Task made by the Rune team. The decided upon approach uses a work-in-progress hyperparameter optimization system called Saga. Saga is a system for creating the best hyperparameter finding in Keras, a popular machine learning framework, using Bayesian optimization and transfer learning. In addition to optimizing the Keras classifier configuration, we try manipulating the dataset by adding extra images in a class lacking in images and splitting a commonly misclassified class into two classes.},
  year = {2018},
  journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
  publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
}