This project is a PyTorch-based generative framework and implementation template for building Generative Adversarial Networks. It provides a collection of foundational toolkits and architectural patterns designed to synthesize high-quality artificial data while focusing on the stability of adversarial neural networks.
The framework distinguishes itself through a specialized toolkit for conditional image generation, which integrates discrete labels and auxiliary classification into the training process. It utilizes specific mechanisms to guide the generative process toward target classes by converting embeddings into image channels and using auxiliary labels for simultaneous authenticity detection and classification.
Broadly, the project covers adversarial model stabilization and training optimization to prevent common failure modes like mode collapse and vanishing gradients. This includes capabilities for gradient flow maintenance, latent space sampling, and training health monitoring via the tracking of gradient norms and loss variance.
The codebase implements a variety of training heuristics, including experience replay buffers, label smoothing, and adaptive optimizer pairings.
