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Here we demonstrate that the feature space of random shallow convolutionalneural networks (CNNs) can serve as a surprisingly good model of naturaltextures. Patches from the same texture are consistently classified as beingmore similar then patches from different textures. Samples synthesized from themodel capture spatial correlations on scales much larger then the receptivefield size, and sometimes even rival or surpass the perceptual quality of stateof the art texture models (but show less variability). The current state of theart in parametric texture synthesis relies on the multi-layer feature space ofdeep CNNs that were trained on natural images. Our finding suggests that suchoptimized multi-layer feature spaces are not imperative for texture modeling.Instead, much simpler shallow and convolutional networks can serve as the basisfor novel texture synthesis algorithms.

Texture Synthesis Using Shallow Convolutional Networks with Random Filters