Implementation of Autoencoding beyond pixels using a learned similarity metric in Keras.
EE298 Group 4;
Peralta, Daryl Mendaros, Jonathan Aslan, Cha-dash
- tensorflow >=1.4
- keras >= 2.1.4
- OpenCV >= 3.4.0
- numpy
For training:
python train.py --dataset [path to dataset]
Ex.
python train.py --dataset /home/daryl/datasets/img_align_celeba
For testing:
python test.py --dataset_path [path dataset] --encoder_path [path to encoder] --decoder_path [path to decoder]
Ex.
python test.py --dataset_path '/home/daryl/datasets/img_align_celeba' --encoder_path checkpoints/encoder_chk-vaegan_complete_demo.hdf5 --decoder_path checkpoints/decoder_chk-vaegan_complete_demo.hdf5 --dataset_path /home/daryl/datasets/img_align_celeba
Checkpoints can be found here.
- 202599 celebrity images
VAEGAN encoder model:
VAEGAN decoder model:
VAEGAN discriminator model:
VAEGAN encoder model for training (only the encoder is trainable):
VAEGAN decoder model for training (only the decoder is trainable):
VAE generation from noise:
VAE autoencoder input:
VAE autoencoder reconstruction:
GAN generation from noise:
VAEGAN generation from noise:
VAEGAN autoencoder input:
VAEGAN autoencoder reconstruction:
VAEGAN generation from noise:
VAEGAN autoencoder input:
VAEGAN autoencoder reconstruction:
VAEGAN generation from noise:
VAEGAN autoencoder input:
VAEGAN autoencoder reconstruction:
