Large Scale Adversarial Representation Learning

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), pp. 10541-10551, 2019.

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Other Links: academic.microsoft.com|dblp.uni-trier.de|arxiv.org

Abstract:

Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to ...More

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