Generative Network

Generative networks are artificial neural networks designed to learn complex data distributions and generate new samples resembling the training data. Current research focuses on improving the quality and diversity of generated outputs, addressing issues like mode collapse and achieving better control over generation through techniques such as conditional generation and network bending. These advancements are driving progress in diverse fields, including image synthesis, 3D modeling, and data augmentation for tasks like anomaly detection and few-shot learning, ultimately enhancing the capabilities of various machine learning applications.