A unified view of gradient-based attribution methods for Deep Neural Networks
M. Ancona, E. Ceolini, A. C. Öztireli, M. Gross
NIPS Workshop on Interpreting, Explaining and Visualizing Deep Learning, Long Beach, USA, 2017
Abstract
Understanding the flow of information in Deep Neural Networks (DNNs) is a
challenging problem that has gain increasing attention over the last few years.
While several methods have been proposed to explain network predictions, only
a few attempts to analyze them from a theoretical perspective have been made in
the past. In this work, we analyze various state-of-the-art attribution methods and
prove unexplored connections between them. We also show how some methods
can be reformulated and more conveniently implemented. Finally, we perform
an empirical evaluation with six attribution methods on a variety of tasks and
architectures and discuss their strengths and limitations.
