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Variational Autoencoder implemented with PyTorch, Trained over CelebA Dataset

Python 100.00%

variational-autoencoder-pytorch's Introduction

Variational Autoencoder for face image generation in PyTorch

Variational Autoencoder for face image generation implemented with PyTorch, Trained over a combination of CelebA + FaceScrub + JAFFE datasets.

Based on Deep Feature Consistent Variational Autoencoder (https://arxiv.org/abs/1610.00291 | https://github.com/houxianxu/DFC-VAE)

TODO: Add DFC-VAE implementation

Pretrained model available at https://drive.google.com/open?id=0B4y-iigc5IzcTlJfYlJyaF9ndlU

Results

Original Faces vs. Reconstructed Faces:

Linear interpolation between two face images:

Vector arithmatic in latent space:

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