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
• Format: text
• Published: Archīum Ateneo 2020
• Subjects: Gallium nitride; Generative adversarial networks; Artificial neural networks; Training; Generators; Neurons; Computer Sciences; Databases and Information Systems
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/277; https://ieeexplore.ieee.org/document/9182445
• Institution: Ateneo De Manila University

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...