Multi WGAN-GP loss for pathological stain transformation using GAN
Multi WGAN-GP loss for pathological stain transformation using GAN
Authors: Atefeh Ziaei Moghadam, Hamed Azarnoush, Seyyed Ali Seyyedsalehi
Publication date: 2021
Publisher: IEEE
Description: In this paper, we proposed a new loss function to train the conditional generative adversarial network (CGAN). CGANs use a condition to generate images. Adding a class condition to the discriminator helps improve the training process of GANs and has been widely used for CGAN. Therefore, many loss functions have been proposed for the discriminator to add class conditions to it. Most of them have the problem of adjusting weights. This paper presents a simple yet new loss function that uses class labels, but no adjusting is required. This loss function is based on WGAN-GP loss, and the discriminator has outputs of the same order (the reason for no adjusting). More specifically, the discriminator has K (the number of classes) outputs, and each of them is used to compute the distance between fake and real samples of one class. Another loss to enable the discriminator to classify is also proposed by applying …
Total citations: 2
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