@inproceedings{reimers2020determining,
    type = {inproceedings},
    key = {reimers2020determining},
    title = {Determining the Relevance of Features for Deep Neural Networks},
    author = {Christian Reimers and Jakob Runge and Joachim Denzler},
    booktitle = {European Conference on Computer Vision},
    year = {2020},
    organization = {Springer},
    pages = {330-346},
    abstract = {Deep neural networks are tremendously successful in many applications, but end-to-end trained networks often result in hard to un- derstand black-box classifiers or predictors. In this work, we present a novel method to identify whether a specific feature is relevant to a clas- sifier’s decision or not. This relevance is determined at the level of the learned mapping, instead of for a single example. The approach does neither need retraining of the network nor information on intermedi- ate results or gradients. The key idea of our approach builds upon con- cepts from causal inference. We interpret machine learning in a struc- tural causal model and use Reichenbach’s common cause principle to infer whether a feature is relevant. We demonstrate empirically that the method is able to successfully evaluate the relevance of given features on three real-life data sets, namely MS COCO, CUB200 and HAM10000.},
    groups = {understanding-dl},
}