A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Abstract
Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance. However, understanding what a network has learned still proves to be a challenging task. To remedy this unsatisfactory situation, a number of groups have recently proposed different methods to visualize the learned models. In this work we suggest a general taxonomy to classify and compare these methods, subdividing the literature into three main categories and providing researchers with a terminology to base their works on. Furthermore, 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.
Cite
@article{arxiv.1606.07757,
title = {A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks},
author = {Felix Grün and Christian Rupprecht and Nassir Navab and Federico Tombari},
journal= {arXiv preprint arXiv:1606.07757},
year = {2024}
}