对抗性自动编码器 (Adversarial Autoencoder, AAE) 的本质 对抗性自动编码器结合了 GAN 和自动编码器（...
在本文中,我们提出了“对抗自动编码器(adversarial autoencoder)”(AAE),这是一种概率自动编码器(probabilistic autoencoder),它使用最近提出的生成对抗网络( generative adversarial networks)(GAN)通过将自动编码器的隐藏代码向量的聚合后验与任意先验分布(arbitrary prior distribution)进行匹配来执行变分推理。 将聚合后...
AAE相比原始GAN而言,显然具有了很多让生成结果更可控的特性,在论文中展示的结果非常不错,我自己在MNIST数据上通生成的图片结果也感觉比之前DCGAN产生的图片质量更高,如下图所示: 不过个人认为,由于Autoencoder本身在分辨率较高的自然图像数据上重构效果就不算太好,因此现阶段还很难把AAE扩展到高分辨率图片数据上,并且...
本文的想法类似《Auto-encoding variational bayes》中变分自动编码器(variational autoencoders,VAE),然而他们使用的是KL散度惩罚的方法在隐藏层编码向量上强加一个先验分布,本文使用的是对抗训练方法去实现该目的,即让隐藏层编码向量的聚合后验能够匹配先验分布。VAE是最小化关于xx的负log似然上边界: 这里聚合后验q(...
由University of Toronto、Google Brain和OpenAI合作的文章Adversarial Autoencoders(AAE)提出了一个使用Autoencoder进行对抗学习的idea,某种程度上对之前这些问题提供了一些新思路,并且包含了Unsupervised、Semi-Supervised和Supervised三种formulation。这篇文章其实自己已经看了有挺长一段时间,不过由于在和另一篇InfoGAN做对比以...
Figure 1. Basic architecture of an AAE. Top row is an autoencoder while the bottom row is an adversarial network which forces the output to the encoder to follow the distribution $p(z)$. On the adversarial regularization part the discriminator recieves $z$ distributed as $q(z|x)$ and $...
To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output. Thanks to our end-to-end training regime, the resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either binary or continuous latent space that covers a much ...
Chainer implementation of adversarial autoencoder (AAE) - adversarial-autoencoder/aae/nn.py at master · musyoku/adversarial-autoencoder
Tensorflow implementation of Adversarial Autoencoders (ICLR 2016) Similar to variational autoencoder (VAE), AAE imposes a prior on the latent variable z. Howerver, instead of maximizing the evidence lower bound (ELBO) like VAE, AAE utilizes a adversarial network structure to guides the model distr...
Any autoencoder network can be turned into a generative model by imposing an arbitrary prior distribution on its hidden code vector. Variational Autoencoder (VAE) [2] uses a KL divergence penalty to impose the prior, whereas Adversarial Autoencoder (AAE) [1] uses {\it generative adversarial ...