kodalinaveen3/DRAGAN
A stable algorithm for GAN training
This algorithm helps machine learning engineers and researchers train Generative Adversarial Networks (GANs) more reliably. By adjusting a few key parameters, it improves the stability of the training process, leading to more consistent and higher-quality synthetic data or images from a given GAN architecture. Users are typically machine learning practitioners experimenting with generative models.
239 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher struggling with the instability and convergence issues when training Generative Adversarial Networks.
Not ideal if you are looking for a pre-trained model or a simple API to generate data without deep engagement in GAN training specifics.
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239
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32
Language
Jupyter Notebook
License
MIT
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Last pushed
Jul 16, 2018
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