Troika GAN vs Decoupled GAN: An Investigation on the Impact of Subnetwork Weight Sharing for Data Augmentation

• Main Authors: Milan, Joe Anthony M, Fernandez, Proceso L, Jr
• 格式: text
• 出版: Archīum Ateneo 2020
• 主題: Gallium nitride; Generative adversarial networks; Artificial neural networks; Training; Generators; Neurons; Computer Sciences; Databases and Information Systems
• 在線閱讀: https://archium.ateneo.edu/discs-faculty-pubs/277; https://ieeexplore.ieee.org/document/9182445

Notable advancements in the field of computer vision have transpired through the application of Generative Adversarial Networks (GANs). A new GAN variant, the Troika GAN (T-GAN), was recently proposed for data augmentation and was shown to be superior to the Coupled GAN (CoGAN) and the classic techn...