cscGAN

How to Quickly Implement Generative Adversarial Networks Using TFGAN

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How to Quickly Implement Generative Adversarial Networks Using TFGAN

Editor｜Debra AI Frontline Introduction: Generative Adversarial Networks (GANs) are currently widely used in various scenarios such as image generation, super-resolution image generation, image compression, image style transfer, data augmentation, and text generation. More and more researchers are engaged in the study of GAN networks, proposing various variants of GAN models, including CGAN, InfoGAN, WGAN, CycleGAN, … Read more

Using Generative Adversarial Networks to Generate and Augment Single-Cell RNA-seq Data

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Using Generative Adversarial Networks to Generate and Augment Single-Cell RNA-seq Data

Nature Communications 2020 Jan 9 IF: 17.694 Introduction: GAN includes a generator that outputs realistic silicon-generated samples. This is achieved through a neural network that learns to transform a simple low-dimensional distribution into a high-dimensional distribution, which is indistinguishable from the actual training distribution. In this paper, the authors establish a single-cell GAN (scGAN) to … Read more