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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]