Skip to main content
SpringerLink
Log in

Auto Data Augmentation for Testing Set

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

Included in the following conference series:

  • 2487 Accesses

Abstract

Testing phase augmentation is a fast way to further improve the performance of image classification when CNN (Convolutional Neural Network) is already trained for hours. Limited attempts have been made to find the best augmentation strategy for testing set. We propose a reinforcement learning based augmentation strategy searching method for testing phase augmentation. With the augmentation strategy, we augment each testing image and integrate features of its augmented images into one feature. The reinforcement learning method searches the best parameters in the augmentation strategy which is formed as a matrix in this paper. Using the proposed method, we achieve competitive accuracies on image classification and face verification.

Wanshun Gao is a student. This work is supported by the National Natural Science Foundation of China (Grant No. 91746111, Grant No.71702143), Ministry of Education & China Mobile Joint Research Fund Program (No. MCM20160302), Shaanxi provincial development and Reform Commission (No. SFG2016789), Xi’an Science and Technology Bureau (No. 2017111SF/RK005-(7)), the Fundamental Research Funds for the Central Universities, Tang Zhongying Foundation for Zhongying Young Scholars.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. JMLR (2013)

    Google Scholar 

  2. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference (2014)

    Google Scholar 

  3. Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)

  4. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. arXiv preprint arXiv:1801.07698 (2018)

  5. DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)

  6. Guo, H., Mao, Y., Zhang, R.: Mixup as locally linear out-of-manifold regularization. arXiv preprint arXiv:1809.02499 (2018)

  7. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst, October 2007

    Google Scholar 

  8. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)

  9. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report. Citeseer (2009)

    Google Scholar 

  10. Liu, S., Zhang, J., Chen, Y., Liu, Y., Qin, Z., Wan, T.: Pixel level data augmentation for semantic image segmentation using generative adversarial networks. arXiv preprint arXiv:1811.00174 (2018)

  11. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  12. Masi, I., Hassner, T., Tran, A.T., Medioni, G.: Rapid synthesis of massive face sets for improved face recognition. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 604–611. IEEE (2017)

    Google Scholar 

  13. Masi, I., Wu, Y., Natarajan, T.H.P.: Deep face recognition: a survey. In: Conference on Graphics, Patterns and Images (SIBGRAPI), October 2018

    Google Scholar 

  14. McLaughlin, N., Del Rincon, J.M., Miller, P.: Data-augmentation for reducing dataset bias in person re-identification. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2015)

    Google Scholar 

  15. Mendoza, H., Klein, A., Feurer, M., Springenberg, J.T., Hutter, F.: Towards automatically-tuned neural networks. In: Workshop on Automatic Machine Learning, pp. 58–65 (2016)

    Google Scholar 

  16. Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51–59 (2017)

    Google Scholar 

  17. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference (2015)

    Google Scholar 

  18. Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameter sharing. In: ICML (2018)

    Google Scholar 

  19. Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: IEEE Conference on Applications of Computer Vision, February 2016

    Google Scholar 

  20. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

    Google Scholar 

  21. Verma, V., et al.: Manifold mixup: learning better representations by interpolating hidden states. Stat 1050, 4 (2018)

    Google Scholar 

  22. Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)

  23. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

  24. Zhu, X., Liu, Y., Li, J., Wan, T., Qin, Z.: Emotion classification with data augmentation using generative adversarial networks. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10939, pp. 349–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93040-4_28

    Chapter  Google Scholar 

  25. Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)

  26. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Wanshun Gao

  2. School of Management, Xi’an Jiaotong University, Xi’an, 710049, China

    Xi Zhao

  3. The Key Lab of the Ministry of Education for Process Control & Efficiency Engineering, Xi’an, 710049, China

    Xi Zhao

Authors
  1. Wanshun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xi Zhao .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, W., Zhao, X. (2019). Auto Data Augmentation for Testing Set. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31723-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation