  An important step towards explaining deep image classifiers lies in the
identification of image regions that contribute to individual class scores in
the model's output. However, doing this accurately is a difficult task due to
the black-box nature of such networks. Most existing approaches find such
attributions either using activations and gradients or by repeatedly perturbing
the input. We instead address this challenge by training a second deep network,
the Explainer, to predict attributions for a pre-trained black-box classifier,
the Explanandum. These attributions are provided in the form of masks that only
show the classifier-relevant parts of an image, masking out the rest. Our
approach produces sharper and more boundary-precise masks when compared to the
saliency maps generated by other methods. Moreover, unlike most existing
approaches, ours is capable of directly generating very distinct class-specific
masks in a single forward pass. This makes the proposed method very efficient
during inference. We show that our attributions are superior to established
methods both visually and quantitatively with respect to the PASCAL VOC-2007
and Microsoft COCO-2014 datasets.
