Conditional Generative Adversarial Networks (cGANs) have been used in many
image processing tasks. However, they still have serious problems maintaining
the balance between conditioning the output on the input and creating the
output with the desired distribution based on the corresponding ground truth.
The traditional cGANs, similar to most conventional GANs, suffer from vanishing
gradients, which backpropagate from the discriminator to the generator.
Moreover, the traditional cGANs are sensitive to architectural changes due to
previously mentioned gradient problems. Therefore, balancing the architecture
of the cGANs is almost impossible. Recently MSG-GAN has been proposed to
stabilize the performance of the GANs by applying multiple connections between
the generator and discriminator. In this work, we propose a method called
MSGDD-cGAN, which first stabilizes the performance of the cGANs using
multi-connections gradients flow. Secondly, the proposed network architecture
balances the correlation of the output to input and the fitness of the output
on the target distribution. This balance is generated by using the proposed
dual discrimination procedure. We tested our model by segmentation of fetal
ultrasound images. Our model shows a 3.18% increase in the F1 score comparing
to the pix2pix version of cGANs.
