shaohua0116/WGAN-GP-TensorFlow
TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.
This project helps machine learning researchers or practitioners generate realistic images from scratch after training a model on existing image datasets. You provide a collection of images (e.g., bedrooms, faces, or street scenes), and it produces new, diverse images that look like they belong to the same dataset. This is useful for tasks requiring synthetic data or creative image generation.
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Use this if you need to generate high-quality, diverse synthetic images from a training dataset and want to leverage advanced Generative Adversarial Network (GAN) techniques for stable training.
Not ideal if you're not comfortable working with Python code and TensorFlow, or if your primary goal is image classification or object detection rather than image generation.
Stars
44
Forks
7
Language
Python
License
MIT
Category
Last pushed
Jun 04, 2019
Commits (30d)
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