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- California Institute of TechnologyUS101 papers
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Showing 2018 · cs.CVShow all
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cs.CV2018★ 10 cited
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Yifan Ding, Liqiang Wang, Deliang Fan +1
The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dat…
cs.CV2018★ 51 cited
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Xiang Wei, Boqing Gong, Zixia Liu +2
Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by \cit…