论文:Autoencoding beyond pixels usingALearnedSimilarityMmetric及视频

语义特征改变、操作：

原视频 https://pan.baidu.com/s/1kV0kbS7

2.3

An appealing property of GAN is that its discriminator net- work implicitly has to learn a rich similarity metric for im- ages, so as to discriminate them from “non-images”. We thus propose to exploit this observation so as to transfer the properties of images learned by the discriminator into a more abstract reconstruction error for the VAE. The end re- sult will be a method that combines the advantage of GAN as a high quality generative model and VAE as a method that produces an encoder of data into the latent space z.

GAN的判别网络需要学习图片中的丰富特征。

因此整合GAN的高质量生成模型优点和VAE的隐变量学习

Specifically, since element-wise reconstruction errors are not adequate for images and other signals with invariances, we propose replacing the VAE reconstruction (expected log likelihood) error term from Eq. 3 with a reconstruction er- ror expressed in the GAN discriminator. To achieve this, let Disl(x) denote the hidden representation of the lth layer of the discriminator. We introduce a Gaussian observation model for Disl(x) with mean Disl(x ̃) and identity covari- ance:

VAE的生成使用GAN。 整合了VAE和GAN。

论文主要内容：

论文贡献：

https://arxiv.org/abs/1512.09300

论文代码：https://github.com/andersbll/autoencoding_beyond_pixels

其他相关代码：https://github.com/timsainb/Tensorflow-MultiGPU-VAE-GAN

https://github.com/staturecrane/dcgan_vae_torch

本文由zdx3578推荐。