Research map / Image generation / Editing and style transfer
MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn a style-neutral initialization with bilevel optimization and adapt it to a style for 200 iterations | MetaStyle · Shared style comparison with Johnson et al. | iterations to obtain a high-quality style transfer model: 160,000 -> 200 | Helped |
| Initialize Gatys-style optimization with the MetaStyle style-neutral representation instead of the content image | Gatys-style optimization · Style transfer comparison | convergence quality | Helped |
| Use bilevel meta-learning with a small number of style-specific update steps | MetaStyle · Arbitrary style transfer | perceptual loss | Helped |
| Adapt the neutral representation to a new style rather than train a single-style model from scratch | MetaStyle · Arbitrary style transfer | adaptation time: 200 iterations; 24 seconds | Helped |
Closest papers by meaning
- Neural Style Transfer via Meta Networks (CVPR 2018 · similarity 0.91)
- ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank (AAAI 2024 · similarity 0.89)
- Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer (CVPR 2024 · similarity 0.89)
- Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network (CVPR 2024 · similarity 0.88)
- OmniStyle: Filtering High Quality Style Transfer Data at Scale (CVPR 2025 · similarity 0.88)
- SigStyle: Signature Style Transfer via Personalized Text-to-Image Models (AAAI 2025 · similarity 0.88)
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