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• W2920684403 abstract "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. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels." @default.
• W2920684403 created "2019-03-11" @default.
• W2920684403 creator A5041958651 @default.
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• W2920684403 date "2019-03-06" @default.
• W2920684403 modified "2023-09-27" @default.
• W2920684403 title "High-Fidelity Image Generation With Fewer Labels" @default.
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• W2920684403 hasPublicationYear "2019" @default.
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