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peiyunh/mat-vae: A MATLAB implementation of Auto-Encoding Variational Bayes

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

peiyunh/mat-vae

开源软件地址(OpenSource Url):

https://github.com/peiyunh/mat-vae

开源编程语言(OpenSource Language):

MATLAB 100.0%

开源软件介绍(OpenSource Introduction):

Variational Auto-Encoder in MATLAB

This is a re-implementation of Auto-Encoding Variational Bayes in MATLAB.

Installation

Data

I use the MNIST from: https://github.com/y0ast/VAE-Torch/tree/master/datasets.

Toolbox

Please install my fork of MatConvNet, where I implemented some new layers, including:

  • KLD.m: handles forward and backward propagation of KL Divergence
  • NLL.m: handles forward and backward propagation of Negative Log-Likelihood (works for multi-variate Bernoulli distribution)
  • LB.m: combine KLD and NLL into a lower bound
  • Sampler.m: sampling operation
  • Tanh.m: tanh non-linearity
  • Split.m: split one variable into multiple while keeping the same spatial size

Usage

Training

For training, please see train_script.m on how I trained models. I implemented four stochastic gradient descent algorithms:

  • SGD with momentum
  • ADAM
  • ADAGRAD
  • RMSPROP

Demo

For demo, I have four demo scripts for visualization under demo/, which are:

  • manifold_demo.m: visualize the manifold of a 2d latent space in image space.
  • sample_demo.m: sample from latent space and visualize in image space.
  • reconstruct_demo.m: visualize a reconstructed version of an input image.
  • walk_demo.m: randomly sample a list of images, and compare the morphing process done in both image space and latent space.

More

To learn about how VAE works under the hood, refer to the original paper or my writeup.




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