disanda/Deep-GAN-Encoders

GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

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Emerging

This project helps researchers and developers working with Generative Adversarial Networks (GANs) to better understand how real-world images relate to the images generated by these networks. It takes an image, processes it through various GAN encoder models (like StyleGAN or BigGAN), and outputs the 'latent code' that best represents the input image within the GAN's creative space. This is for machine learning researchers and practitioners interested in image generation, manipulation, and GAN interpretability.

No commits in the last 6 months.

Use this if you need to map a real image into the latent space of a pre-trained GAN (like StyleGAN or BigGAN) to analyze or manipulate its features.

Not ideal if you are looking for an easy-to-use, off-the-shelf image editing tool for end-users, or if you don't have a background in machine learning and GANs.

generative AI research image synthesis latent space exploration image manipulation GAN interpretability
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 8 / 25
Community 17 / 25

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Stars

49

Forks

10

Language

Python

License

Last pushed

May 18, 2024

Commits (30d)

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