gordicaleksa/pytorch-GANs

My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN (Goodfellow et al.), cGAN (Mirza et al.), DCGAN (Radford et al.), etc.

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This project helps anyone interested in learning how Generative Adversarial Networks (GANs) work by providing practical, clear examples. It takes in real image datasets, like handwritten digits, and produces new, synthetic images that look authentic. This tool is ideal for machine learning students, researchers, or enthusiasts who want to understand GANs by seeing them in action and generating new content.

388 stars. No commits in the last 6 months.

Use this if you are a beginner interested in understanding and experimenting with different GAN architectures to generate realistic images.

Not ideal if you need a production-ready system for complex image generation tasks or advanced research that requires highly specialized GAN variations.

generative-modeling image-synthesis machine-learning-education neural-networks data-generation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 19 / 25

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Stars

388

Forks

58

Language

Python

License

MIT

Last pushed

Dec 07, 2020

Commits (30d)

0

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