Art Arena evaluates how artistic styles from training data leak into AI-generated images without explicit prompts, revealing asymmetric blending due to differences in representational strength and interaction dynamics across models like Stable Diffusion.
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A Neural Algorithm of Artistic Style
15 Pith papers cite this work. Polarity classification is still indexing.
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abstract
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
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representative citing papers
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Systematic benchmark reveals recent complex INR methods for continuous image super-resolution offer only marginal gains, with performance tied to training setups, auxiliary losses improving textures, and scaling laws holding.
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DMT uses identity and makeup encoders in a GAN to enable controllable makeup transfer from references and sampling of new styles from a prior distribution.
Incidental multilingualism from uneven web training makes LLMs unequal, brittle, and opaque across languages.
Universal aesthetic alignment in image models biases outputs toward conventional beauty and penalizes anti-aesthetic prompts even when they match explicit user instructions.
ArtNet and PhotoNet enable one-pass fast universal style transfer with fewer artifacts, better detail preservation, and 3-100x speedup over prior AE-based methods.
A layered image-processing pipeline with illumination transfer enables real-time facial makeup application from a single reference image while handling dark makeup and air-bangs.
DeepTEGINN is a deep learning toolbox combining image processing and graph theory to automate graph extraction from brain tissue images as an alternative to manual tracing.
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The Silent Brush: Evaluating Artistic Style Leakage in AI Art Generation
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Corruptions of Supervised Learning Problems: Typology and Mitigations
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Fast Universal Style Transfer for Artistic and Photorealistic Rendering
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