Autoencoding beyond pixels using a learned similarity metric

Understand

We present an autoencoder that leverages learned representations to better measure similarities in data space.

• By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective.
• Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g.
• translation.