High-Fidelity Image Generation With Fewer Labels

6 Mar 2019Mario Lucic • Michael Tschannen • Marvin Ritter • Xiaohua Zhai • Olivier Bachem • Sylvain Gelly

Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data.

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