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Consistent Arbitrary Style Transfer Using Consistency Training and Self-Attention Module.

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    Summary
    This summary is machine-generated.

    This study introduces a Consistent Arbitrary Style Transfer (CAST) framework to address style inconsistencies in image generation. CAST effectively captures essential style features for reliable and state-of-the-art style transfer.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Arbitrary Style Transfer (AST) aims for infinite style transfer but often suffers from inconsistencies due to minor disturbances.
    • Existing methods may overlook crucial style patterns, leading to unreliable style transfer (ST).

    Purpose of the Study:

    • To develop a novel framework for consistent arbitrary style transfer (CAST).
    • To quantify and mitigate style inconsistencies in generated images.

    Main Methods:

    • Mathematical analysis of inconsistent ST and development of a Style Inconsistency Measure (SIM).
    • Proposed CAST framework integrating an Intersection-of-Union-Preserving Crop (IoUPC) module, a Self-Attention (SA) module, and Style Inconsistency Loss Regularization (SILR).

    Main Results:

    • The CAST framework effectively captures and transfers essential style features, ensuring consistency.
    • Experiments show CAST outperforms existing methods in consistent style transfer.
    • Demonstrated state-of-the-art performance in preserving style patterns and consistency.

    Conclusions:

    • The proposed CAST framework offers an optimal solution for consistent arbitrary style transfer.
    • CAST framework significantly enhances the reliability and quality of style transfer applications.