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Much effort has been devoted to understanding the decisions of deep neuralnetworks in recent years. A number of model-aware saliency methods wereproposed to explain individual classification decisions by creating saliencymaps. However, they are not applicable when the parameters and the gradients ofthe underlying models are unavailable. Recently, model-agnostic methods havealso received attention. As one of them, \textit{Prediction DifferenceAnalysis} (PDA), a probabilistic sound methodology, was proposed. In this work,we first show that PDA can suffer from saturated classifiers. The saturationphenomenon of classifiers exists widely in current neural network-basedclassifiers. To explain the decisions of saturated classifiers better, wefurther propose Contextual PDA, which runs hundreds of times faster than PDA.The experiments show the superiority of our method by explaining imageclassifications of the state-of-the-art deep convolutional neural networks.

Contextual Prediction Difference Analysis for Explaining Individual Image Classifications