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
October 24, 2022
October 19, 2022
October 14, 2022
September 25, 2022
September 20, 2022
September 14, 2022
August 12, 2022
August 5, 2022
August 2, 2022
July 25, 2022
July 21, 2022
July 12, 2022
July 4, 2022
July 2, 2022
July 1, 2022
June 28, 2022
June 27, 2022
June 23, 2022