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In many machine learning applications, from medical diagnostics to autonomousdriving, the availability of prior knowledge can be used to improve thepredictive performance of learning algorithms and incorporate `physical,'`domain knowledge,' or `common sense' concepts into training of machinelearning systems as well as verify constraints/properties of the systems. Weexplore the learning using privileged information paradigm and show how toincorporate the privileged information, such as segmentation mask availablealong with the classification label of each example, into the training stage ofconvolutional neural networks. This is done by augmenting the CNN model with anarchitectural component that effectively focuses model's attention on thedesired region of the input image during the training process and that istransparent to the network's label prediction mechanism at testing. Thiscomponent effectively corresponds to the visualization strategy for identifyingthe parts of the input, often referred to as visualization mask, that mostcontribute to the prediction, yet uses this strategy in reverse to theclassical setting in order to enforce the desired visualization mask instead.We verify our proposed algorithms through exhaustive experiments on benchmarkImageNet and PASCAL VOC data sets and achieve improvements in the performanceof $2.4\%$ and $2.7\%$ over standard single-supervision model training.Finally, we confirm the effectiveness of our approach on skin lesionclassification problem.

VisualBackProp for learning using privileged information with CNNs