Active domain adaptation with noisy labels for multimedia analysis


Supervised learning methods require sufficient labeled examples to learn a good model for classification or regression. However, available labeled data are insufficient in many applications. Active learning (AL) and domain adaptation (DA) are two strategies to minimize the required amount of labeled data for model training. AL requires the domain expert to label a small number of highly informative examples to facilitate classification, while DA involves tuning the source domain knowledge for classification on the target domain. In this paper, we demonstrate how AL can efficiently minimize the required amount of labeled data for DA. Since the source and target domains usually have different distributions, it is possible that the domain expert may not have sufficient knowledge to answer each query correctly. We exploit our active DA framework to handle incorrect labels provided by domain experts. Experiments with multimedia data demonstrate the efficiency of our proposed framework for active DA with noisy labels.

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This work was partially supported by the MIUR Cluster project Active Ageing at Home, the EC project xLiMe and A*STAR Singapore under the Human-Centered Cyber-physical Systems (HCCS) grant.

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Correspondence to Yan Yan.

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Liu, G., Yan, Y., Subramanian, R. et al. Active domain adaptation with noisy labels for multimedia analysis. World Wide Web 19, 199–215 (2016).

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  • Active learning
  • Domain adaptation
  • Noisy labels
  • Multimedia analysis