Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen; Søren Kaae Sønderby; Hugo Larochelle; Ole Winther

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arXiv · 1512.09300

Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen·Søren Kaae Sønderby·Hugo Larochelle·Ole Winther

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Abstract

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.

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cs.LG cs.CV stat.ML

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Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther. 2016-02-10. Autoencoding beyond pixels using a learned similarity metric. https://arxiv.org/abs/1512.09300

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