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This paper presents a novel approach to deep-dream-like image generation for convolutional neural networks (CNNs). Images are produced by a deep generative network from a smaller dimensional feature vector. This method allows for the generation of more realistic looking images than traditional activation-maximization methods and gives insight into the CNN's internal representations. Training is achieved by standard backpropagation algorithms.doi:10.5220/0006495102150222 dblp:conf/ijcci/DesprazGSP17 fatcat:smntn4qz6zhvfjuu7a2asjyydq