The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. The training of the GAN model is changed so that the generator is provided both with a point in the latent space and a class label as input, and attempts to generate an image for that class. Recent Related Work Generative adversarial networks have been vigorously explored in the last two years, and many conditional variants have been proposed. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products. Make GAN useful. The benefit of the Pix2Pix model is that compared to other GANs for conditional image generation, it is relatively simple and capable Generator — Given a label and random array as input, this network generates data with the same structure as the training data … A conditional GAN, cGAN or CGAN for short, is an extension of the GAN architecture that adds structure to the latent space. Sarus TF2 Models - A long list of recent generative models implemented in clean, easy to reuse, Tensorflow 2 code (Plain Autoencoder, VAE, VQ-VAE, PixelCNN, Gated PixelCNN, PixelCNN++, PixelSNAIL, Conditional Neural Processes). TensorFlow 2 in Action by Manning - Code Repository - Exercises of all the chapters in TensorFlow 2 in Action by Manning paper [Conditional Generative Adversarial Nets] Loss: blog [wiseodd/conditional-gan-tensorflow] (Fomulation, Architecture, Implementation) code [wiseodd/conditional_gan](star 500+, very simple, 1 file, easy to read and run, not conv, inconvinient to extend) G: concat(z,y)-->fc-->sigmoid D: concat(z,y)-->fc-->sigmoid loss A conditional generative adversarial network (CGAN) is a type of GAN that also takes advantage of labels during the training process.

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