Adversarial Auto-Encoder for MNIST

An implementation of adversarial auto-encoder (AAE) for MNIST descripbed in the paper:

• Adversarial Autoencoders by Alireza Makhzani et al.

Implementation Details

The paper suggest various ways of using AAE.

• Basic AAE
• Incorporatiing Label Information in the Adversarial Regularization
• Supervised AAE
• Semi-supervised AAE
• Unsupervised Clustering with AAE
• Dimensionality Reduction with AAE

Only results on 'Incorporatiing Label Information in the Adversarial Regularization' are given here.

Target Distributions

Three types of prior distrubtion are considered.

• a mixture of 10 2-D Gaussians
• a swiss roll distribution
• a normal distribution : not suggested in the paper.

The following graphs can be obtained with command:

python test_prior_type.py --prior_type <type>

mixGaussian	swiss_roll	normal

Results

Leveraging label information to better regularize the hidden code in Figure 4 in the paper.

Prior distribution type : a mixture of 10 2-D Gaussians

The following results can be reproduced with command:

python run_main.py --prior_type mixGaussian

Learned MNIST manifold (20 Epochs)	Distribution of labeled data (20 Epochs)

Prior distribution type : a swiss roll distribution

The following results can be reproduced with command:

python run_main.py --prior_type swiss_roll

Learned MNIST manifold (20 Epochs)	Distribution of labeled data (20 Epochs)

Prior distribution type : a normal distribution (not suggested in the paper)

The following results can be reproduced with command:

python run_main.py --prior_type normal

Learned MNIST manifold (20 Epochs)	Distribution of labeled data (20 Epochs)

Usage

Prerequisites

1. Tensorflow
2. Python packages : numpy, scipy, PIL(or Pillow), matplotlib

Command

python run_main.py --prior_type <type>

Arguments

Required :

• --prior_type: The type of prior distrubition. Choices: mixGaussian, swiss_roll, normal. Default: mixGaussian

Optional :

• --results_path: File path of output images. Default: results
• --n_hidden: Number of hidden units in MLP. Default: 1000
• --learn_rate: Learning rate for Adam optimizer. Default: 1e-3
• --num_epochs: The number of epochs to run. Default: 20
• --batch_size: Batch size. Default: 128
• --PRR: Boolean for plot-reproduce-result. Default: True
• --PRR_n_img_x: Number of images along x-axis. Default: 10
• --PRR_n_img_y: Number of images along y-axis. Default: 10
• --PRR_resize_factor: Resize factor for each displayed image. Default: 1.0
• --PMLR: Boolean for plot-manifold-learning-result. Default: True
• --PMLR_n_img_x: Number of images along x-axis. Default: 15
• --PMLR_n_img_y: Number of images along y-axis. Default: 15
• --PMLR_resize_factor: Resize factor for each displayed image. Default: 1.0
• --PMLR_z_range: Range for unifomly distributed latent vector. Default: 3.0
• --PMLR_n_samples: Number of samples in order to get distribution of labeled data. Default: 10000

Acknowledgements

This implementation has been tested with Tensorflow 1.2.1 on Windows 10.