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Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise. However, the reasons for its effectiveness are not well understood. In this study, we investigate the role played by different properties of the PI in explaining away label noise. Through experiments on multiple datasets with real PI (CIFAR-N/H) and a new large-scale benchmark ImageNet-PI, we find that PI is most helpful when it allows networks to easily distinguish clean from noisy data, while enabling a learning shortcut to memorize the noisy examples. Interestingly, when PI becomes too predictive of the target label, PI methods often perform worse than their no-PI baselines. Based on these findings, we propose several enhancements to the state-of-the-art PI methods and demonstrate the potential of PI as a means of tackling label noise. Finally, we show how we can easily combine the resulting PI approaches with existing no-PI techniques designed to deal with label noise.
Author Information
Guillermo Ortiz Jimenez (EPFL)
Mark Collier (Google)
Anant Nawalgaria (Research, Google)
Alexander D'Amour (Google DeepMind)
Jesse Berent (Google)
Rodolphe Jenatton (Google Research)
Efi Kokiopoulou (Google AI)
Efi is a research scientist at Google since February 2013. She joined Google as a PostDoc researcher in September 2011. Before that she was a postdoctoral research fellow at the Seminar for Applied Mathematics (SAM) at ETH, Zurich. She completed her PhD studies in December 2008 at the Signal Processing Laboratory (LTS4) of the Swiss Federal Institute of Technology (EPFL), Lausanne under the supervision of Prof. Pascal Frossard. Before that she was with the Computer Science & Engineering Department of the University of Minnesota, USA, where she obtained in June 2005 her M.Sc. degree under the supervision of Prof. Yousef Saad. She obtained B.Eng. and MscEng. degrees in 2002 and 2003 respectively at the Computer Engineering and Informatics Department of the University of Patras, Greece.
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