  Conventionally, AI models are thought to trade off explainability for lower
accuracy. We develop a training strategy that not only leads to a more
explainable AI system for object classification, but as a consequence, suffers
no perceptible accuracy degradation. Explanations are defined as regions of
visual evidence upon which a deep classification network makes a decision. This
is represented in the form of a saliency map conveying how much each pixel
contributed to the network's decision. Our training strategy enforces a
periodic saliency-based feedback to encourage the model to focus on the image
regions that directly correspond to the ground-truth object. We quantify
explainability using an automated metric, and using human judgement. We propose
explainability as a means for bridging the visual-semantic gap between
different domains where model explanations are used as a means of disentagling
domain specific information from otherwise relevant features. We demonstrate
that this leads to improved generalization to new domains without hindering
performance on the original domain.
