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Generative Adversarial Interpolative Autoencoder (GAIA) is a Generative Adversarial Network (GAN) made up of Autoencoders (AE) trained explicitly on interpolations to promote convexity and better latent interpolations.
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readme.md

Generative Adversarial Interpolative Autoencoding (GAIA)

UPDATE: see an updated version of this code trained on Fashion MNIST

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:

  1. GANs are not bidirectional
  2. Autoencoders produce blurry images
  3. Autoencoder latent spaces are not convex

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Instructions

  1. Download the GAIA dataset with the notebook 'Create_CELEBA-HQ.ipynb'
  2. Download the trained weights 'download_weights.ipynb'
  3. 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.

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References

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