NicelyCla/cWGAN-gp
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
This is a Pytorch implementation of a conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-gp). It's a machine learning tool that takes in existing datasets to learn their underlying distribution, enabling the generation of new, realistic synthetic data. Researchers and machine learning practitioners focused on generative modeling would use this.
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Use this if you are a machine learning researcher or practitioner needing to generate synthetic data conditional on specific inputs, and you are familiar with advanced GAN architectures.
Not ideal if you are looking for a simple, out-of-the-box data generation solution without deep expertise in generative adversarial networks or deep learning frameworks.
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Jun 19, 2022
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