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To what extent is the success of deep visualization due to the training?Could we do deep visualization using untrained, random weight networks? Toaddress this issue, we explore new and powerful generative models for threepopular deep visualization tasks using untrained, random weight convolutionalneural networks. First we invert representations in feature spaces andreconstruct images from white noise inputs. The reconstruction quality isstatistically higher than that of the same method applied on well trainednetworks with the same architecture. Next we synthesize textures using scaledcorrelations of representations in multiple layers and our results are almostindistinguishable with the original natural texture and the synthesizedtextures based on the trained network. Third, by recasting the content of animage in the style of various artworks, we create artistic images with highperceptual quality, highly competitive to the prior work of Gatys et al. onpretrained networks. To our knowledge this is the first demonstration of imagerepresentations using untrained deep neural networks. Our work provides a newand fascinating tool to study the representation of deep network architectureand sheds light on new understandings on deep visualization.

A Powerful Generative Model Using Random Weights for the Deep Image Representation