  It is a mystery which input features contribute to a neural network's output.
Various explanation (feature attribution) methods are proposed in the
literature to shed light on the problem. One peculiar observation is that these
explanations (attributions) point to different features as being important. The
phenomenon raises the question, which explanation to trust? We propose a
framework for evaluating the explanations using the neural network model
itself. The framework leverages the network to generate input features that
impose a particular behavior on the output. Using the generated features, we
devise controlled experimental setups to evaluate whether an explanation method
conforms to an axiom. Thus we propose an empirical framework for axiomatic
evaluation of explanation methods. We evaluate well-known and promising
explanation solutions using the proposed framework. The framework provides a
toolset to reveal properties and drawbacks within existing and future
explanation solutions.

Machine Learning;Computer Vision and Pattern Recognition