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  1. GitHub - poloclub/ganlab: GAN Lab: An Interactive, Visual ...

    GAN Lab is a novel interactive visualization tool for anyone to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, you …

  2. gan · GitHub Topics · GitHub

    Nov 20, 2025 · 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 …

  3. GitHub - Yangyangii/GAN-Tutorial: Simple Implementation of many …

    Simple Implementation of many GAN models with PyTorch. - Yangyangii/GAN-Tutorial

  4. 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 …

  5. 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 …

  6. 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 …

  7. 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 …

  8. generative-adversarial-network · GitHub Topics · GitHub

    May 18, 2024 · Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator …

  9. 如何形象又有趣的讲解对抗神经网络(GAN)是什么? - 知乎

    GAN 是神经网络的一种,是由被誉为“生成对抗网络之父” 的蒙特利尔大学博士生伊恩·古德弗洛(Ian Goodfellow)在2014年提出的。 百度的前首席科学家吴恩达(Andrew Ng)说,GAN 代表了“重要 …

  10. The GAN is dead; long live the GAN! A Modern Baseline GAN (R3GAN)

    Code for NeurIPS 2024 paper - The GAN is dead; long live the GAN! A Modern Baseline GAN - by Huang et al. - brownvc/R3GAN