Adversarial AutoEncoder
- Code for the paper
Requirements
- Chainer 2+
- Python 2 or 3
Incorporating Label Information in the Adversarial Regularization
run semi-supervised/regularize_z/train.py
We trained with a prior (a mixture of 10 2-D Gaussians or Swissroll distribution) on 10K labeled MNIST examples and 40K unlabeled MNIST examples.
Supervised Adversarial Autoencoders
run supervised/learn_style/train.py
Semi-Supervised Adversarial Autoencoders
run semi-supervised/classification/train.py
| data | # |
|---|---|
| labeled | 100 |
| unlabeled | 49900 |
| validation | 10000 |
Validation accuracy at each epoch
Analogies
Unsupervised clustering
run unsupervised/clustering/train.py
16 clusters
32 clusters
Dimensionality reduction
run unsupervised/dim_reduction/train.py
run semi-supervised/dim_reduction/train.py
