Generative Adversarial Interpolative Autoencoding (GAIA)
Authors: Tim Sainburg, Marvin Thielk, Brad Theilman, Benjamin Migliori, Tim Gentner (UCSD)
The Generative Adversarial Interpolative Autoencoder (GAIA; Paper; Blog post) is novel hybrid between the Generative Adversarial Network (GAN) and the Autoencoder (AE). The purpose of GAIA is to address three issues which exist in GANs and AEs:
- GANs are not bidirectional
- Autoencoders produce blurry images
- Autoencoder latent spaces are not convex
- Download the GAIA dataset with the notebook 'Create_CELEBA-HQ.ipynb'
- Download the trained weights 'download_weights.ipynb'
- Run the notebook 'GAIA simple example.ipynb'
Note: I'm currently in the process of rewriting this code to be cleaner, include more features, etc. For now, this is just the version of the code used in the Arxiv paper.
- Multimodal Unsupervised Image-to-Image Translation (Paper; Author implementation; Tensorflow implementation)
- Progressively Growing GANs (Paper, Code)