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, Volume 77, Issue 20, pp 26563–26580 | Cite as

Stacked multichannel autoencoder – an efficient way of learning from synthetic data

  • Xi Zhang
  • Yanwei Fu
  • Shanshan Jiang
  • Xiangyang Xue
  • Yu-Gang Jiang
  • Gady Agam
Article
  • 71 Downloads

Abstract

Learning from synthetic data has many important applications in case where sufficient amounts of labeled data are not available. Using synthetic data is challenging due to differences in feature distributions between synthetic and actual data, a phenomenon we term synthetic gap. In this paper, we investigate and formalize a general framework – Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently. In particular, we show that our SMCAE can not only transform and use synthetic data on a challenging face-sketch recognition task, but that it can also help simulate real images which can be used for training classifiers for recognition. Preliminary experiments validate the effectiveness of the proposed framework.

Keywords

Multimodal autoencoder Synthetic gap Satellite image classification Learning from synthetic data Face-sketch recognition 

Notes

Acknowledgements

This work is supported by Fudan University-CIOMP Joint Fund (FC2017-006). Yanwei Fu is supported by The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (No. TP2017006). Yanwei Fu is the corresponding author.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xi Zhang
    • 1
  • Yanwei Fu
    • 2
    • 4
  • Shanshan Jiang
    • 1
  • Xiangyang Xue
    • 3
  • Yu-Gang Jiang
    • 3
    • 4
  • Gady Agam
    • 1
  1. 1.Illinois Institute of TechnologyChicagoUSA
  2. 2.School of Data ScienceFudan UniversityShanghaiChina
  3. 3.School of Computer ScienceFudan UniversityShanghaiChina
  4. 4.The Academy for Engineering and TechnologyFudan UniversityShanghaiChina

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