{"data":{"full_name":"YuvanJain/WGAN","name":"WGAN","description":"A web-based implementation of a Wasserstein Generative Adversarial Network (WGAN) built with PyTorch and Flask, designed to generate realistic images through stable training using the Wasserstein loss. The project demonstrates how advanced deep learning models can be integrated into an interactive web application, making it easy to experiment with.","stars":0.0,"forks":0.0,"language":"Jupyter Notebook","license":null,"archived":0.0,"subcategory":"gan-architectures-implementations","last_pushed_at":"2026-03-17T11:34:00+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":13.0,"adoption_score":0.0,"maturity_score":1.0,"community_score":0.0,"quality_score":14.0,"quality_tier":"experimental","risk_flags":"['no_license', 'no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-07T09:48:44.952653+00:00"}}