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• W2896923958 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." @default.
• W2896923958 created "2018-10-26" @default.
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• W2896923958 date "2018-10-09" @default.
• W2896923958 modified "2023-09-27" @default.
• W2896923958 title "SPIGAN: Privileged Adversarial Learning from Simulation" @default.
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• W2896923958 hasPublicationYear "2018" @default.
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