We introduce the FeatureVis library for MatConvNet: an extendable, easy to use open source library for visualizing CNNs. It contains implementations from each of the three main classes of visualization methods and serves as a useful tool for an enhanced understanding of the features learned by intermediate layers, as well as for the analysis of why a network might fail for certain examples.
The code and documentation can be found on GitHub.
|F. Grün, C. Rupprecht, N. Navab, F. Tombari
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
International Conference on Machine Learning (ICML) Workshop on Visualization for Deep Learning, New York, USA, June 23rd, 2016 (bib)
This work was partly funded by TUM - Institute for Advanced Study (German Excellence Initiative - FP7 Grant 291763)