Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Paper summary This paper presents methods for visualizing the behaviour of an object recognition convolutional neural network. The first method generates a "canonical image" for a given class that the network can recognize. The second generates a saliency map for a given input image and specified class, that illustrates the part of the image (pixels) that influence the most the given class's output probability. This can be used to seed a graphcut segmentation and localize objects of that class in the input image. Finally, a connection between the saliency map method and the work of Zeiler and Fergus on using deconvolutions to visualize deep networks is established.
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew
arXiv e-Print archive - 2013 via Bibsonomy
Keywords: dblp


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