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    This study introduces a novel collaborative framework for face photo-sketch synthesis, enhancing accuracy and speed. The model learns a shared latent domain, improving the symmetry and quality of generated sketches compared to existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Recent advancements in face photo-sketch synthesis leverage deep convolutional neural networks.
    • Existing unidirectional models (photo-to-sketch and sketch-to-photo) lack mutual interaction, limiting performance.

    Purpose of the Study:

    • To propose a collaborative framework for face photo-sketch synthesis that utilizes the interaction between opposite mappings.
    • To improve the accuracy, speed, and quality of generated face sketches.

    Main Methods:

    • Developed a collaborative framework incorporating a middle latent domain (~Z) between photo (X) and sketch (Y) domains.
    • Introduced a collaborative loss function to enforce symmetry between the G: X → Y and F: Y → X mappings.
    • Employed end-to-end convolutional neural networks for regression-based synthesis.

    Main Results:

    • The collaborative framework demonstrated superior performance over state-of-the-art methods.
    • Experiments showed enhanced quality in generated face sketches.
    • The model achieved higher accuracy and speed in synthesis tasks.

    Conclusions:

    • The proposed collaborative framework effectively leverages mutual interaction between photo and sketch domains.
    • Learning a shared latent domain and enforcing symmetrical mappings leads to improved face photo-sketch synthesis.
    • This approach offers a promising direction for generating higher-quality synthetic sketches.