1. In a digital medium classification environment, a method implemented by at least one computing device, the method comprising: aggregating, by the at least one computing device, patterns of neurons in a neural network by progressing through a sequence of layers of the neural network to classify an image as relating to a semantic class; communicating, by the at least one computing device, positive relevancy of the patterns formed by the neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the communicating of the positive relevancy of the pattern between a plurality of layers from the sequence of layers is based on a probabilistic Winner-Take-All (WTA) approach; localizing, by the at least one computing device, the semantic class within the image based on the communicated positive relevancy of the aggregated patterns to the semantic class; and generating, by the at least one computing device, digital content based on localization of the semantic class within the image.