-
Implementation of Autoencoding beyond pixels using a learned similarity metric, based on the Tensorflow implementation: https://github.com/JeremyCCHsu/tf-vaegan
-
Please refer to their official Github for details: Autoencoding Beyond Pixels
-
As the name indicates, VAE-GAN replaces GAN's generator with a variational auto-encoder, resulting in a model with both inference and generation components.
- Dataset: caltech 101 silhouettes dataset from https://people.cs.umass.edu/~marlin/data.shtml
- Opencv
- Python packages required: scipy, scikit-learn and Pillow, opencv python package
Deep Learning AMI (Ubuntu) - 2.0, p2.8xlarge
If you want to train and test with the default options do the following:
- Download the default dataset and convert from matlab file format to png file format
python convert_data.py
- Train on the downloaded dataset and store the encoder model and generator model params.
python vaegan_mxnet.py --train
- Test on the downloaded dataset
python vaegan_mxnet.py --test --testing_data_path /home/ubuntu/datasets/caltech101/test_data
- Using existing models:
python vaegan_mxnet.py --test --testing_data_path [your dataset image path] --pretrained_encoder_path [pretrained encoder model path] --pretrained_generator_path [pretrained generator model path] [options]
- Train a new model:
python vaegan_mxnet.py --train --training_data_path [your dataset image path] [options]
- Training on the CPU:
python vaegan_mxnet.py --train --use_cpu --training_data_path [your dataset image path] [options]