Semantic class localization techniques and systems are described. In one or more implementation, a technique is employed to back communicate relevancies of aggregations back through layers of a neural network. Through use of these relevancies, activation relevancy maps are created that describe relevancy of portions of the image to the classification of the image as corresponding to a semantic class. In this way, the semantic class is localized to portions of the image. This may be performed through communication of positive and not negative relevancies, use of contrastive attention maps to different between semantic classes and even within a same semantic class through use of a self-contrastive technique.
Please help me write a proper abstract based on the patent claims. CLAIM: 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. 2. The method as described in claim 1, wherein the semantic class identifies an object included in the image or emotional feeling expressed in the image. 3. (canceled) 4. The method as described in claim 1, wherein the communicating of the positive relevancy from one said neuron in a first said layer through connections to a plurality of said neurons in a second said layer is based on whether respective said connections between respective said neurons are: excitatory as pertaining to the aggregated pattern of the semantic class; or inhibitory as not pertaining to the aggregated pattern of the semantic class. 5. The method as described in claim 1, wherein the positive relevancy of the patterns to the semantic class is defined using an activation relevancy map of the image. 6. The method as described in claim 5, wherein the activation relevancy map describes a winning probability of connections between the neurons in different said layers as relating to positive relevancy to the semantic class. 7. The method as described in claim 5, wherein: the communicating including communicating a contrastive activation relevancy map that is self-contrastive as describing a lack of relevancy of respective said neurons in a respective said layer to the semantic class; and the localizing is based at least in part on the aggregated patterns, the activation relevancy map, and the contrastive activation relevancy map. 8. The method as described in claim 7, wherein the localizing includes removing portions of the aggregated patterns in the activation relevancy map that are in common with corresponding said neurons of patterns in the contrastive activation relevancy map that describe the lack of relevancy of the respective said neurons in the respective said layer. 9. The method as described in claim 1, wherein: the positive relevancy of the patterns to the semantic class is defined using an activation relevancy map of the image; the communicating includes communicating a contrastive activation relevancy map that describes relevancy of aggregated patterns of the neurons to another semantic class; and the localizing is based at least in part on the aggregated patterns, the activation relevancy map, and the contrastive activation relevancy map. 10. The method as described in claim 1, wherein the communicating of the positive relevancy through the sequence of layers is performed as a single operation. 11. The method as described in claim 1, wherein the communicating of the positive relevancy preserves a sum of values of the positive relevancy between the sequence of layers. 12. 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, an activation relevancy map and a contrastive activation relevancy map by progressing backwards through the sequence of layers of the neural network, wherein the activation relevancy map describes relevancy of the activations of the neurons in a layer to the semantic class and the contrastive activation relevancy map is self-contrastive as describing a lack of relevancy of the activations of the neurons in a layer to the semantic class; localizing, by the at least one computing device, the semantic class within the image based on the aggregated patterns, the activation relevancy map, and the contrastive activation relevancy map, wherein the localizing includes cancelling relevancy of said neurons of the activation relevancy map that are in common with said neurons of the contrastive activation relevancy map; and generating, by the at least one computing device, digital content defining the localizing of the semantic class within the image. 13. (canceled) 14. (canceled) 15. (canceled) 16. The method as described in claim 12, wherein the relevancy is communicated from one said neuron in a first said layer through connections to a plurality of said neurons in a second said layer is based on whether respective said connections between respective said neurons are: excitatory as pertaining to the aggregated pattern of the semantic class; or inhibitory as not pertaining to the aggregated pattern of the semantic class. 17. In a digital medium environment supportive of image search, a system comprising: a neural network implemented at least partially using processing hardware, the neural network having a plurality of layers; an aggregation module implemented at least partially using processing hardware to cause the neural network to aggregate patterns of neurons by progressing through a sequence of layers, the patterns classifying an image as relating to a semantic class; a back propagation module implemented at least partially using processing hardware to cause the neural network to communicate relevancy of the patterns of the neurons as a positive and not negative relevancy to the semantic class by progressing backwards through the sequence of layers; a digital content generation module implemented at least partially using processing hardware to generate digital content that localizes the semantic class within the image based at least in part on the aggregated patterns of neurons and the relevancy of the patterns of the neurons to the semantic class; and an image search module implemented at least partially using processing hardware to perform a search using a plurality of items of said digital content to locate respective said images as corresponding to text of a search query. 18. The system as described in claim 17, wherein the back propagation module is configured to communicate the relevancy from one said neuron in a first said layer through connections to a plurality of said neurons in a second said layer based on whether respective said connections between respective said neurons are: excitatory as pertaining to the aggregated pattern of the semantic class; or inhibitory as not pertaining to the aggregated pattern of the semantic class. 19-20. (canceled) 21. The system as described in claim 17, wherein the positive relevancy of the patterns between respective said layers of the neural network is based on a probabilistic Winner-Take-All (WTA) approach. 22. The system as described in claim 17, wherein the positive relevancy of the patterns to the semantic class is defined using an activation relevancy map of the image. 23. 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 respective said neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the positive relevancy of the patterns to the semantic class is defined using an activation relevancy map of the image, wherein the communicating including communicating a contrastive activation relevancy map that is self-contrastive as describing a lack of relevancy of respective said neurons in a respective said layer to the semantic class; 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, wherein the localizing is based at least in part on the aggregated patterns, the activation relevancy map, and the contrastive activation relevancy map; and generating, by the at least one computing device, digital content based on localization of the semantic class within the image. 24. The method as described in claim 23, wherein the localizing includes removing portions of the aggregated patterns in the activation relevancy map that are in common with corresponding said neurons of patterns in the contrastive activation relevancy map that describe the lack of relevancy of the respective said neurons in the respective said layer. 25. 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 respective said neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the positive relevancy of the patterns to the semantic class is defined using an activation relevancy map of the image, wherein communicating includes communicating a contrastive activation relevancy map that describes relevancy of aggregated patterns of the neurons to another semantic class; 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, wherein the localizing is based at least in part on the aggregated patterns, the activation relevancy map, and the contrastive activation relevancy map; and generating, by the at least one computing device, digital content based on localization of the semantic class within the image. 26. 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 respective said neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the communicating of the positive relevancy through the plurality of layers is performed as a single operation; 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. 27. 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 respective said neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the communicating of the positive relevancy preserves a sum of values of the positive relevancy between the plurality of layers; 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. 28. 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, an activation relevancy map and a contrastive activation relevancy map by progressing backwards through the sequence of layers of the neural network, wherein: the activation relevancy map describes relevancy of the activations of respective said neurons in a respective said layer to the semantic class; and the contrastive activation relevancy map describes relevancy of activations of respective said neurons in the respective said layer to another semantic class; localizing, by the at least one computing device, the semantic class within the image based on the aggregated patterns, the activation relevancy map, and the contrastive activation relevancy map; and generating, by the at least one computing device, digital content defining the localizing of the semantic class within the image.