Conditional Generative Adversarial Nets

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Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.

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Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

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github.com: /soyoung9306/GAN

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CGAN(https://arxiv.org/pdf/1411.1784.pdf) implementation in MXNet. Refered to https://github.com/znxlwm/pytorch-MNIST-CelebA-cGAN-cDCGAN

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The repository contains example code for conditional Generative Adversarial Model in theno and Lasagne. The code follows the paper https://arxiv.org/abs/1411.1784 but with a different deep architecture. The folder Generative Images contains Some sampled generated images from the code.

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[CSC722 Project] Data-driven recipe recommendation system using web-scraped recipe data.

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Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

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Keras implementation of the conditional GAN.

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Conditional GAN for Anime face generation.

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A list of all named GANs!

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The codes of VAE-GAN model for 3d shape reconstruction from depth data

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[CSC722 Project] Data-driven recipe recommendation system using web-scraped recipe data.

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Github: YadiraF/GAN

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Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

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The test code for Conditional convolutional adversarial networks.

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Generative Adversarial Networks and its variations tried on not MNIST dataset.

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Generative Adversarial Networks

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Exploration of CNN GANs and Conditional CNN GANs on the MNIST dataset

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Implementation of several Generative Adversarial Networks in tensorflow.

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A simple conditional version of the Boundary Equilibrium Generative Adversarial Networks (CBEGANs)

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