basiclab/GNGAN-PyTorch
Official implementation for Gradient Normalization for Generative Adversarial Networks
This project helps machine learning researchers and practitioners working with Generative Adversarial Networks (GANs) to generate high-quality synthetic images. By implementing Gradient Normalized GAN (GN-GAN), it takes datasets like CIFAR-10, STL-10, CelebA-HQ, or LSUN Church and outputs trained models capable of producing realistic new images from those distributions. This is primarily for those looking to improve GAN training stability and output quality.
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Use this if you are a machine learning researcher or engineer developing or experimenting with GANs and want to improve the stability and performance of your image generation models.
Not ideal if you are looking for a plug-and-play solution to generate images without deep technical understanding of GAN architectures or training processes.
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Python
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Last pushed
Oct 11, 2021
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