318 citations · 825 across the 71 of their papers we have counts for
Showing 2016 · cs.CVShow all
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cs.CV2016
Learning from Simulated and Unsupervised Images through Adversarial Training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel +3
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from…
cs.CV2016★ 102 cited
Coupled Generative Adversarial Networks
Ming-Yu Liu, Oncel Tuzel
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of…
cs.CV2016★ 21 cited
Global-Local Face Upsampling Network
Oncel Tuzel, Yuichi Taguchi, John R. Hershey
Face hallucination, which is the task of generating a high-resolution face image from a low-resolution input image, is a well-studied problem that is useful in widespread applicati…