MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Generative adversarial networks (GANs) adversarially train generative and discriminative and generate a nonexistent images. Common GANs use only a single generative model and discriminant model and are considered to maximize their performance. On the other hand, in the image-classification task, recognition accuracy improves by collaborative learning in which knowledge transfer is conducted among several neural networks. Therefore, we propose a method that involves using GANs with multiple generative models and one discriminant model to conduct collaborative learning while transferring information among the generative models. We conducted experiments to evaluate the proposed method, and the results indicate that the quality of the images produced by the proposed method is improved and increased in diversity.