2024年8月24日 · 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 …

gan最经常看到的例子就是斑马和马的互相转换了，相信你即使不知道gan是什么，也曾见过这个例子。 GAN简介 GAN的想法非常巧妙，它会创建两个不同的对立的网络，目的是让一个网络生成与训练集不同的且足以让另外一个网络难辨真假的样本。

Simple Implementation of many GAN models with PyTorch. Topics pytorch gan mnist infogan dcgan regularization celeba wgan began wgan-gp infogan-pytorch conditional-gan pytorch-gan gan-implementations vanilla-gan gan-pytorch 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. In the adversarial learning of N real training samples and M generated samples, the target of discriminator training is to distribute all the probability mass to the real samples, each ...

TF-GAN is composed of several parts, which are designed to exist independently: Core : the main infrastructure needed to train a GAN. Set up training with any combination of TF-GAN library calls, custom-code, native TF code, and other frameworks

Abstract: There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim and build a modern GAN baseline in a more principled manner. First, we derive a well-behaved regularized ...

2023年4月12日 · gan生成对抗网络 是一种深度学习模型，由两个神经网络 共同组成：生成器和判别器。这两个网络通过对抗的方式进行训练，其中生成器尝试伪造逼真的样本数据，而判别器则负责判断输入的数据是真实数据还是生成器伪造出来的数据。

GAN的出现通过对抗学习使得网络学到更精准的Loss，促使生成器生成更高质量的结果，大大推动了这一领域的发展，使其进入更多人的视野。 然而GAN的不稳定性和理论实践中的一些gap使得人们试图找到更好的用于生成模型的技术，比如之前的Glow就是很不错的工作。

GAN before using JS divergence has the problem of non-overlapping, leading to mode collapse and convergence difficulty. Use EM distance or Wasserstein-1 distance, so GAN solve the two problems above without particular architecture (like dcgan).

2024年5月18日 · 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 …