Artificial intelligence is reshaping the world . This technology is changing the way we handle our daily tasks. Deep learning is one method to go toward artificial intelligence and it has so far shown great significance when applied to various areas, from medicine to computer vision.  However, deep learning showed also that it can be used to harm or help in fraudulence, depending on how it is applied. One example of this is what is called Generative adversarial learning . In 2016, generative adversarial networks (GANs) were presented to the world, and since then many versions of these networks were developed. A GAN can be defined as a machine learning model that consists of two neural networks competing with each other. These two networks are called generator and discriminator, and each has a different role; one is for generating images from random noise given as input, while the latter is for detecting whether the generated images are real or fake.  Finally, this whole process is re

  Introduction Image segmentation is the partitioning of images into various regions, in which every region has a different entity. An efficient tool for image segmentation is a convolutional neural network (CNN) . Recently, there has been a significant impact of CNNs that are designed to perform image segmentation. One the best models presented was the U-Net . A U-Net is U-shaped convolutional neural network that was originally designed to segment biomedical images. Such a network is better than conventional models, in terms of architecture and pixel-based image segmentation formed from convolutional neural network layers. Similar to all CNNs, this network consists of convolution, Max-pooling, and ReLU activation layers. However, in a general view, U-Net can be seen as an encoder-decoder network. The encoder is the first part of this network and it is a conventional convolutional neural network like VGG or ResNet that is composed of convolution, pooling, and downsampling laye