This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns.
The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep Belief Network implementations.
The library covers broad capabilities in probabilistic data modeling and unsupervised representation learning. It includes tools for synthetic data generation and the use of energy-based networks to model binary data distributions.
