Search

In order to mitigate mode collapse when generative adversarial networks (GANs) synthesis images, this paper proposed maximizing middle modes and minor modes in generative adversarial networks (MMMGANs). First of all, MMMGANs defined the mode of the generated images with the identical label as the middle mode and the minor mode. The middle mode and the minor mode separately represented the collection of similar modes and the possible mode changes after learning the middle modes. Secondly, based on the above definition, this paper proposed maximizing middle modes and minor modes loss. Finally, on the premise that the distribution of generated images was possibly close to the real image, MMMGANs improved more than 90% of evaluation metrics and then enhanced diversity of generated images. Extensive experimental results show that the proposed maximizing middle modes and minor modes loss effectively alleviates mode collapse in two or more different types of tasks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)