How to iNNvestigate neural networks' predictions!

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans

Keywords: machine learning, artificial intelligence, explaining, analyzing, neural networks, deep learning

Abstract:

In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes such as autonomous driving or medical image analysis. In these and many other domains it is crucial that specialists can understand and analyze actions and predictions, even of the most complex neural network architectures. Despite these arguments neural networks are often treated as black boxes and their complex internal workings as well as the basis for their predictions are not fully understood.
In the attempt to alleviate this shortcoming many analysis methods were proposed, yet the lack of reference implementations often makes a systematic comparison between the methods a major effort. In this tutorial we present the library iNNvestigate which addresses the mentioned issue by providing a common interface and out-of-the-box implementation for many analysis methods. In the first part we will show how iNNvestigate enables users to easily compare such methods for neural networks. The second part will demonstrate how the underlying API abstracts a common operations in neural network analysis and show how users can use them for the development of (future) methods.

iNNvestigate and the tutorial resources are available at: https://github.com/albermax/innvestigate

Decision: accept