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arXiv · 1810.03756

SPIGAN: Privileged Adversarial Learning from Simulation

Kuan-Hui Lee·German Ros·Jie Li·Adrien Gaidon
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Abstract

Deep Learning for Computer Vision depends mainly on the source of supervision.Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce a domain gap negatively impacting performance. We propose anew unsupervised domain adaptation algorithm, called SPIGAN, relying on Sim-ulator Privileged Information (PI) and Generative Adversarial Networks (GAN).We use internal data from the simulator as PI during the training of a target tasknetwork. We experimentally evaluate our approach on semantic segmentation. Wetrain the networks on real-world Cityscapes and Vistas datasets, using only unla-beled real-world images and synthetic labeled data with z-buffer (depth) PI fromthe SYNTHIA dataset. Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.

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Kuan-Hui Lee, German Ros, Jie Li, Adrien Gaidon. 2018-10-09. SPIGAN: Privileged Adversarial Learning from Simulation. https://arxiv.org/abs/1810.03756

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