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  • GitHub - tensorflow gan: Tooling for GANs in TensorFlow
    TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs) Can be installed with pip using pip install tensorflow-gan, and used with import tensorflow_gan as tfgan Well-tested examples Interactive introduction to TF-GAN in
  • 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
  • GAN生成对抗网络D_loss和G_loss到底应该怎样变化? - 知乎
    做 GAN 有一段时间了,可以回答下这个问题。 G是你的任务核心,最后推理用的也是G,所以G的LOSS是要下降收敛接近0的,G的目标是要欺骗到D。 而成功的训练中,由于要达到G欺骗D的目的,所以D的Loss是不会收敛的,在G欺骗D的情况下,D的LOSS会在0 5左右。
  • Diffusion-GAN — Official PyTorch implementation - 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
  • 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)
  • 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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