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

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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.

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Notes

  1. δ is a sparsity parameter and is empirically set to 0.05 in all our experiments.

  2. Collected from UCI machine learning repository (HWDUCI) [3].

  3. The parameters are cross-validated

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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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Authors and Affiliations

  1. Illinois Institute of Technology, Chicago, IL, 60616, USA

    Xi Zhang, Shanshan Jiang & Gady Agam

  2. School of Data Science, Fudan University, Shanghai, China

    Yanwei Fu

  3. School of Computer Science, Fudan University, Shanghai, China

    Xiangyang Xue & Yu-Gang Jiang

  4. The Academy for Engineering and Technology, Fudan University, Shanghai, China

    Yanwei Fu & Yu-Gang Jiang

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  1. Xi Zhang
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  2. Yanwei Fu
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  3. Shanshan Jiang
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  4. Xiangyang Xue
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  5. Yu-Gang Jiang
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Correspondence to Yanwei Fu.

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Zhang, X., Fu, Y., Jiang, S. et al. Stacked multichannel autoencoder – an efficient way of learning from synthetic data. Multimed Tools Appl 77, 26563–26580 (2018). https://doi.org/10.1007/s11042-018-5879-7

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  • DOI: https://doi.org/10.1007/s11042-018-5879-7

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