Generative Adversarial
Generative Adversarial Networks (GANs) are a class of machine learning models designed to generate new data instances that resemble a training dataset. Current research focuses on improving GAN performance and stability across diverse applications, including image enhancement, speech synthesis, and data augmentation, often employing architectures like HiFi-GAN and variations of GANs combined with other neural network types (e.g., autoencoders, transformers). This work is significant due to GANs' ability to address data scarcity issues, improve the quality of synthetic data for various tasks, and enhance the robustness of AI systems against adversarial attacks.
Papers
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks
Munsif Ali, Leonardo Rossi, Massimo Bertozzi
Generative Adversarial Synthesis of Radar Point Cloud Scenes
Muhammad Saad Nawaz, Thomas Dallmann, Torsten Schoen, Dirk Heberling
Golyadkin's Torment: Doppelgängers and Adversarial Vulnerability
George I. Kamberov