Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data

CVPR 2018 Arghya PalVineeth N Balasubramanian

Paucity of large curated hand-labeled training data for every domain-of-interest forms a major bottleneck in the deployment of machine learning models in computer vision and other fields. Recent work (Data Programming) has shown how distant supervision signals in the form of labeling functions can be used to obtain labels for given data in near-constant time... (read more)

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