No correlation exists between CNNs' Brain-Score alignment with the visual system and the perceptual content of their Gram-matrix texture representations.
Incorporating long-range consistency in CNN-based texture generation
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abstract
Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate long-range structure into image generation, and to render images that satisfy various symmetry constraints. We show how this can greatly improve rendering of regular textures and of images that contain other kinds of symmetric structure. We also present applications to inpainting and season transfer.
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cs.CV 1years
2026 1verdicts
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Perceptual misalignment of texture representations in convolutional neural networks
No correlation exists between CNNs' Brain-Score alignment with the visual system and the perceptual content of their Gram-matrix texture representations.