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October 2, 2025Open Access

Stylized Structural Patterns for Improved Neural Network Pre-training

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Pretraining on enhanced synthetic datasets leads to an 11% reduction in FID during image generation.
Our approach shows a significant reduction in the distributional gap compared to existing synthetic datasets.
A ViT-S model trained on this synthetic data achieves over a 10% improvement in ImageNet-100 accuracy.
Our method utilizes reverse stylization to improve data representation and enable effective model training.

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This paper demonstrates how improved synthetic data reduces domain gap in neural networks, indicating potential for enhanced model training.

Salehi et al. (2025) studied this question.

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This paper demonstrates how improved synthetic data reduces domain gap in neural networks, indicating potential for enhanced model training.