A single reference-guided flow-matching model synthesizes continuous-scale (3–20×) high-detail garment images from unaligned full-view and close-up product photos, matching per-instance fine-tuning quality at far lower cost.
Texture synthesis usingconvolutionalneuralnetworks
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abstract
Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. The model provides a new tool to generate stimuli for neuroscience and might offer insights into the deep representations learned by convolutional neural networks.
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Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
Three style-based neural architectures are proposed for real-time weather classification from images, with two truncated ResNet variants claimed to outperform prior methods and generalize across public datasets.
citing papers explorer
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GarmentZoom: Generating Zoomable Images from Garment Listings
A single reference-guided flow-matching model synthesizes continuous-scale (3–20×) high-detail garment images from unaligned full-view and close-up product photos, matching per-instance fine-tuning quality at far lower cost.
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
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Style-Based Neural Architectures for Real-Time Weather Classification
Three style-based neural architectures are proposed for real-time weather classification from images, with two truncated ResNet variants claimed to outperform prior methods and generalize across public datasets.