Awesome
Deep Directed Generative Models with Energy-Based Probability Estimation
code for the paper
この記事で実装したコードです。
Requirements
- Chainer 1.17
- PIL
- pylab
Contains the following repository:
2D datasets
Train generator to generate 10 dimensional Gaussian mixture distribution and swiss-roll distribution.
See videos:
Running
run train_2d/train.py
to train the model.
run train_2d/gif_gaussian.py
or train_2d/gif_swissroll.py
to generate gif frames.
MNIST
run train_mnist/train.py
If there is no MNIST image, it will be downloaded automatically.
Genereted images
killmebaby(キルミーベイベー)
Download 686 images from http://killmebaby.tv/special_icon.html and resize all to 64x64 pixels.
run train_killmebaby/train.py
Original images
Images generated by Deep Generative Model
Since the position of the face of the training data is not constant, I think it is difficult to train the generator, but relatively clean images are generated.
When learning of Generator did not go well
Whichever noise z is used to generate an image, an average is generated.