TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Schall, Martin
A1  - Sacha, Dominik
A1  - Stein, Manuel
A1  - Franz, Matthias O.
A1  - Keim, Daniel
T1  - Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition
T2  - Symposium on Visualization in Data Science (VDS) at IEEE VIS 2018, 22. Okt. 2018, Berlin
N2  - Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-parameters in their data selection, transformation, network topology and training process that are sometimes interdependent. This increases the overall difficulty and time necessary for building and training a model for a specific data set and task at hand. This work proposes a novel visualization-assisted workflow that guides the model developer through the hyper-parameter search in order to identify relevant parameters and modify them in a meaningful way. This decreases the overall time necessary for building and training a model. The contributions of this work are a workflow for hyper-parameter search in offline handwriting recognition and a heat map based visualization technique for deep neural networks in multi-line offline handwriting recognition. This work applies to offline handwriting recognition, but the general workflow can possibly be adapted to other tasks as well.
Y1  - 2018
UR  - http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1prqtf0ga5nfs7
SP  - 8
S1  - 8
ER  -