Autoencoding beyond pixels using a learned similarity metric
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. We apply our method to images of faces and show that it outperforms VAEs with element wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.
ICML