Authors: Nicola Pezzotti, Thomas Höllt, Jan van Gemert, Boudewijn P, F, Lelieveldt, Elmar Eisemann, Anna Vilanova
Abstract: Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional \ classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of \ handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as \ the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic \ design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform \ due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports \ the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a \ stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as \ superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system \ through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.