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- gan · GitHub Topics · GitHub
gan Generative adversarial networks (GAN) are a class of generative machine learning frameworks A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training
- 生成对抗网络（GAN） - 知乎
生成对抗网络 (Generative Adversarial Network, GAN) 是一类神经网络，通过轮流训练判别器 (Discriminator) 和生成器 (Generator)，令其相互对抗，来从复杂概率分布中采样，例如生成图片、文字、语音等。GAN 最初由 Ian Goodfellow 提出，原论文见 [1406 2661] Generative Adversarial Networks
- 如何形象又有趣的讲解对抗神经网络（GAN）是什么? - 知乎
GAN在过去几年里已成为深度学习中最热门的子领域之一，Yann LeCun说GAN是过去10年机器学习最有趣的想法。 看完后，你应该对： GAN是什么 具体要做一个简单的GAN应该怎么做 GAN能做啥 都很清楚了！ 目录： GAN简介 (与图灵学习和纳什均衡的关系) 使用“垃圾邮件识别“进行详细说明 (定义混淆矩阵
- GitHub - eriklindernoren PyTorch-GAN: PyTorch implementations of . . .
Softmax GAN is a novel variant of Generative Adversarial Network (GAN) The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross-entropy loss in the sample space of one single batch
- GitHub - Yangyangii GAN-Tutorial: Simple Implementation of many GAN . . .
Simple Implementation of many GAN models with PyTorch - Yangyangii GAN-Tutorial
- Zhendong-Wang Diffusion-GAN - GitHub
This paper introduces Diffusion-GAN that employs a Gaussian mixture distribution, defined over all the diffusion steps of a forward diffusion chain, to inject instance noise A random sample from the mixture, which is diffused from an observed or generated data, is fed as the input to the discriminator
- The GAN is dead; long live the GAN! A Modern Baseline GAN (R3GAN) - GitHub
Code for NeurIPS 2024 paper - The GAN is dead; long live the GAN! A Modern Baseline GAN - by Huang et al - brownvc R3GAN
- GitHub - tkarras progressive_growing_of_gans: Progressive Growing of . . .
The Progressive GAN code repository contains a command-line tool for recreating bit-exact replicas of the datasets that we used in the paper The tool also provides various utilities for operating on the datasets:
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