GAN Inversion: A brief walkthrough — Part I

1. Background

1.1 Generative Adversarial Networks

Eq. 1: GAN objective function proposed by [2]
Fig. 1: Generative Adversarial Network Architecture

1.2 GAN latent space

Fig. 2: The W space proposed by [4]
Eq. 2: Linear interpolation defined by [3]
Fig. 3: Latent space interpolation results from [3]

2. GAN inversion and approaches

2.1 GAN inversion

Fig. 4: GAN inversion from [1]
Eq. 3: Inversion problem objective function

2.2 GAN inversion approaches

Eq. 4: Optimization-based GAN inversion
Fig. 5: Optimization-based GAN inversion from [1]
Eq. 5: Learning-based GAN inversion
Fig. 6: Learning-based GAN inversion from [1]
Fig. 7: Hybrid GAN inversion from [1]

3. Differences from VAE-GANs

Fig. 8: VAE-GAN architecture by [8]

4. Summary

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