  In many machine learning applications, from medical diagnostics to autonomous
driving, the availability of prior knowledge can be used to improve the
predictive performance of learning algorithms and incorporate `physical,'
`domain knowledge,' or `common sense' concepts into training of machine
learning systems as well as verify constraints/properties of the systems. We
explore the learning using privileged information paradigm and show how to
incorporate the privileged information, such as segmentation mask available
along with the classification label of each example, into the training stage of
convolutional neural networks. This is done by augmenting the CNN model with an
architectural component that effectively focuses model's attention on the
desired region of the input image during the training process and that is
transparent to the network's label prediction mechanism at testing. This
component effectively corresponds to the visualization strategy for identifying
the parts of the input, often referred to as visualization mask, that most
contribute to the prediction, yet uses this strategy in reverse to the
classical setting in order to enforce the desired visualization mask instead.
We verify our proposed algorithms through exhaustive experiments on benchmark
ImageNet and PASCAL VOC data sets and achieve improvements in the performance
of $2.4\%$ and $2.7\%$ over standard single-supervision model training.
Finally, we confirm the effectiveness of our approach on skin lesion
classification problem.
