  We propose three segmentation methods using GAN and information leakage
between generator and discriminator. First, we propose an Adversarial Training
Attention Module (ATA-Module) that uses an attention mechanism from the
discriminator to the generator to enhance and leak important information in the
discriminator. ATA-Module transmits important information to the generator from
the discriminator. Second, we propose a Top-Down Pixel-wise Difficulty
Attention Module (Top-Down PDA-Module) that leaks an attention map based on
pixel-wise difficulty in the generator to the discriminator. The generator
trains to focus on pixel-wise difficulty, and the discriminator uses the
difficulty information leaked from the generator for classification. Finally,
we propose an Adversarial Mutual Leakage Network (AML-Net) that mutually leaks
the information each other between the generator and the discriminator. By
using the information of the other network, it is able to train more
efficiently than ordinary segmentation models. Three proposed methods have been
evaluated on two datasets for cell image segmentation. The experimental results
show that the segmentation accuracy of AML-Net was much improved in comparison
with conventional methods.
