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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 680–696Cite as

Dual Adversarial Network for Deep Active Learning

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12369))

Abstract

Active learning, reducing the cost and workload of annotations, attracts increasing attentions from the community. Current active learning approaches commonly adopted uncertainty-based acquisition functions for the data selection due to their effectiveness. However, data selection based on uncertainty suffers from the overlapping problem, i.e., the top-K samples ranked by the uncertainty are similar. In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration. In particular, we propose a dual adversarial network, namely DAAL, for this purpose. Different from previous hybrid active learning methods requiring multi-stage data selections i.e., step-by-step evaluating the uncertainty and representativeness using different acquisition functions, our DAAL learns to select the most uncertain and representative data points in one-stage. Extensive experiments conducted on three publicly available datasets, i.e., CIFAR10/100 and Cityscapes, demonstrate the effectiveness of our method—a new state-of-the-art accuracy is achieved.

S. Wang—Intern at Tencent Jarvis Lab.

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Notes

  1. 1.

    Summary is a sparse subset of video frames which optimally represent the input video.

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Author information

Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Shuo Wang, Ruhui Ma & Haibing Guan

  2. Tencent Jarvis Lab, Shenzhen, China

    Shuo Wang, Yuexiang Li, Kai Ma & Yefeng Zheng

Authors
  1. Shuo Wang
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  2. Yuexiang Li
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  3. Kai Ma
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  4. Ruhui Ma
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  5. Haibing Guan
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  6. Yefeng Zheng
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Corresponding authors

Correspondence to Yuexiang Li or Ruhui Ma .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Wang, S., Li, Y., Ma, K., Ma, R., Guan, H., Zheng, Y. (2020). Dual Adversarial Network for Deep Active Learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_40

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  • DOI: https://doi.org/10.1007/978-3-030-58586-0_40

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