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Found 8 papers, 2 papers with code
Learning Structural Similarity of User Interface Layouts using Graph Networks
We propose a novel representation learning technique for measuring the similarity of user interface designs.
DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer
Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image.
ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer
Representation learning aims to discover individual salient features of a domain in a compact and descriptive form that strongly identifies the unique characteristics of a given sample respective to its domain.
NeAT: Neural Artistic Tracing for Beautiful Style Transfer
As a component of curating this data, we present a novel model able to classify if an image is stylistic.
PARASOL: Parametric Style Control for Diffusion Image Synthesis
We propose PARASOL, a multi-modal synthesis model that enables disentangled, parametric control of the visual style of the image by jointly conditioning synthesis on both content and a fine-grained visual style embedding.
HyperNST: Hyper-Networks for Neural Style Transfer
We present HyperNST; a neural style transfer (NST) technique for the artistic stylization of images, based on Hyper-networks and the StyleGAN2 architecture.
StyleBabel: Artistic Style Tagging and Captioning
We present StyleBabel, a unique open access dataset of natural language captions and free-form tags describing the artistic style of over 135K digital artworks, collected via a novel participatory method from experts studying at specialist art and design schools.
ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity
We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style.
