Restricting the Flow: Information Bottlenecks for Attribution
This provides a more reliable and interpretable attribution method for researchers and practitioners using neural networks, though it is incremental as it builds on existing information bottleneck theory.
The authors tackled the problem of interpreting neural network decisions by adapting the information bottleneck concept to attribution, quantifying information from image regions in bits, and outperformed ten baselines in five out of six settings on VGG-16 and ResNet-50.
Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method's information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision. For reviews: https://openreview.net/forum?id=S1xWh1rYwB For code: https://github.com/BioroboticsLab/IBA