Variational_Autoencoder

Variational Autoencoder(VAE)

If you have interesting ideas or data, please contact me quickly at wangzichaochaochao@gmail.com .

Variational autoencoders are interesting generative models, which combine ideas from deep learning with statistical inference. They can be used to learn a low dimensional representation Z of high dimensional data X such as images (of e.g. faces). In contrast to standard auto encoders, X and Z are random variables. It’s therefore possible to sample X from the distribution P(X| Z), thus creating e.g. images of faces, MNIST Digits, or speech.

Here we use VAE to realize face generation.

变分自动编码器是有趣的生成模型，它将深度学习和统计推断的思想结合起来。 它们可用于学习高维数据X的低维表示Z，例如图像（例如面部）。 与标准自动编码器相比，X和Z是随机变量。 因此，可以从分布P（X | Z）中对X进行采样，从而产生例如 面部图像，MNIST数字或语音。

这儿使用 VAE 来实现人脸生成功能。

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代码用法 Code usage

You can use your own data or directly use the data of a predefined task.

你可以使用自己的数据或者直接使用预定义任务的数据。

CelebFaces Attributes (CelebA) Dataset https://www.kaggle.com/jessicali9530/celeba-dataset/home

Require

• python 3+, e.g. python==3.6
• tensorflow version 2, e.g. tensorflow==2.0.0
• tensorflow-datasets

Train Model

python train_vae_model.py

Model Inference

python inference_by_vae_model.py

Relevant Reading of Variational Autoencoder

• 变分自编码器（望江人工智库）
• Introduction to variational autoencoders

2019 VQ-VAE-2

The above simple Convolutional Variational Autoencoder can only generate blurred face images. If you want to get better results, we recommend VQ-VAE-2, which can generate the following high-definition images.

上述简单的卷积变分自编码器只能生成模糊的人脸图片，如果你想获得更好的效果，推荐使用VQ-VAE-2，它能生成如下的高清图像。

• Paper Generating Diverse High-Fidelity Images with VQ-VAE-2
• Code vq-vae-2-pytorch
• Youtube AI Creates Near Perfect Images Of People, Dogs and More
• Demo