Grabel P, Nickel G, Crysandt M, Herwartz R, Baumann M, Klinkhammer BM, Boor P, Brummendorf TH, Merhof D (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings
Event location: Iowa City, IA, USA
ISBN: 9781728174013
DOI: 10.1109/ISBIWorkshops50223.2020.9153408
Performance and robustness of neural networks depend on a suitable choice of hyper-parameters, which is important in research as well as for the final deployment of deep learning algorithms. While a manual systematical analysis can be too time consuming, a fully automatic search is very dependent on the kind of hyper-parameters. For a cell classification network, we assess the individual effects of a large number of hyper-parameters and compare the resulting choice of hyperparameters with state of the art search techniques. We further propose an approach for automated, successive search space reduction that yields well performing sets of hyperparameters in a time-efficient way.
APA:
Grabel, P., Nickel, G., Crysandt, M., Herwartz, R., Baumann, M., Klinkhammer, B.M.,... Merhof, D. (2020). Systematic Analysis and Automated Search of Hyper-Parameters for Cell Classifier Training. In ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings. Iowa City, IA, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Grabel, Philipp, et al. "Systematic Analysis and Automated Search of Hyper-Parameters for Cell Classifier Training." Proceedings of the 17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020, Iowa City, IA, USA Institute of Electrical and Electronics Engineers Inc., 2020.
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