Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

• URL: http://arxiv.org/abs/2002.03754v3
• Date: Wed, 24 Jun 2020 12:12:14 GMT
• Title: Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
• Authors: Andrey Voynov, Artem Babenko
• Abstract summary: latent spaces of GAN models often have semantically meaningful directions. We introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. We show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection.
• Score: 39.54530450932134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently performed in a supervised manner, requiring human labels, pretrained models, or some form of self-supervision. These requirements severely restrict a range of directions existing approaches can discover. In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. By a simple model-agnostic procedure, we find directions corresponding to sensible semantic manipulations without any form of (self-)supervision. Furthermore, we reveal several non-trivial findings, which would be difficult to obtain by existing methods, e.g., a direction corresponding to background removal. As an immediate practical benefit of our work, we show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection.

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