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Inverting the Generator of a Generative Adversarial Network.

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    Summary
    This summary is machine-generated.

    This study introduces an inversion technique to map images back into the latent space of generative adversarial networks (GANs). This method quantifies GAN performance and identifies modeled data attributes.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision

    Background:

    • Generative adversarial networks (GANs) synthesize high-dimensional data but lack an inverse mapping from data to latent space.
    • This limitation hinders the inference of latent representations for specific data samples.

    Purpose of the Study:

    • To introduce an inversion technique for projecting data samples, particularly images, into the latent space of pretrained GANs.
    • To enable the identification of attributes modeled by GANs and quantify their performance.

    Main Methods:

    • Developed an inversion technique to map image data back to the latent space of a trained GAN.
    • Utilized reconstruction loss to evaluate GAN performance and identify modeled attributes.
    • Applied the technique to compare multiple GAN models across three image datasets.

    Main Results:

    • Successfully projected images into the latent space of pretrained GANs.
    • Quantified GAN performance and identified specific data attributes captured by the models.
    • Demonstrated the utility of the inversion technique for comparative analysis of GANs.

    Conclusions:

    • The proposed inversion technique provides a method to analyze and quantify the capabilities of trained GANs.
    • This approach facilitates a deeper understanding of GANs' latent space properties and performance.
    • The technique enables quantitative comparison between different GAN models.