Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Similarity-Based Pattern Recognition

SIMBAD 2015: Similarity-Based Pattern Recognition pp 84–92Cite as

Deep Metric Learning Using Triplet Network

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9370))

Abstract

Deep learning has proven itself as a successful set of models for learning useful semantic representations of data. These, however, are mostly implicitly learned as part of a classification task. In this paper we propose the triplet network model, which aims to learn useful representations by distance comparisons. A similar model was defined by Wang et al. (2014), tailor made for learning a ranking for image information retrieval. Here we demonstrate using various datasets that our model learns a better representation than that of its immediate competitor, the Siamese network. We also discuss future possible usage as a framework for unsupervised learning.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Bengio, Y.: Learning Deep Architectures for AI (2009). ISSN 1935–8237

    Google Scholar 

  2. Bengio, Y.: Deep learning of representations: looking forward. In: Dediu, A.-H., Martín-Vide, C., Mitkov, R., Truthe, B. (eds.) SLSP 2013. LNCS, vol. 7978, pp. 1–37. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  3. Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., Shah, R.: Signature verification using a time delay neural network. Int. J. Pattern Recogn. Artif. Intell. 7(04), 669–688 (1993)

    Article  Google Scholar 

  4. Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109–1135 (2010)

    MathSciNet  MATH  Google Scholar 

  5. Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 539–546 (2005). ISBN 0769523722

    Google Scholar 

  6. Coates, A., Ng, A.Y., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)

    Google Scholar 

  7. Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: A matlab-like environment for machine learning. In: BigLearn, NIPS Workshop, number EPFL-CONF-192376 (2011)

    Google Scholar 

  8. Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks (2013). arXiv preprint arXiv:1302.4389

  9. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE computer society conference on Computer vision and pattern recognition, vol. 2, pp. 1735–1742. IEEE (2006)

    Google Scholar 

  10. Hinton, G.E.: Learning multiple layers of representation (2007). ISSN 13646613

    Google Scholar 

  11. Krizhevsky, A. Hinton, G.: Learning multiple layers of features from tiny images. Computer Science Department, University of Toronto, Technical report (2009)

    Google Scholar 

  12. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  13. Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets (2014). arXiv preprint arXiv:1409.5185

  14. Lin, M., Chen, Q., Yan, S.: Network in network. CoRR, abs/1312.4400 (2013). http://arxiv.org/abs/1312.4400

  15. Lin, T.-H. Kung, H.T.: Stable and efficient representation learning with nonnegativity constraints. In Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1323–1331 (2014)

    Google Scholar 

  16. Mairal, J., Koniusz, P., Harchaoui, Z., Schmid, C.: Convolutional kernel networks. In: Advances in Neural Information Processing Systems, pp. 2627–2635 (2014)

    Google Scholar 

  17. Mobahi, H., Collobert, R., Weston, J.: Deep learning from temporal coherence in video. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 737–744. ACM (2009)

    Google Scholar 

  18. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)

    Google Scholar 

  19. Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN Features off-the-shelf: an Astounding Baseline for Recognition (2014). Arxiv http://arxiv.org/abs/1403.6382

  20. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat : Integrated Recognition, Localization and Detection using Convolutional Networks, pp. 1–15 (2013). arXiv preprint arXiv:1312.6229, http://arxiv.org/abs/1312.6229

  21. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR, abs/1409.4842 (2014). http://arxiv.org/abs/1409.4842

  22. Tamuz, O., Liu, C., Belongie, S., Shamir, O., Kalai, A.: Adaptively learning the crowd kernel. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML 2011, pp. 673–680. ACM, New York (2011). ISBN 978-1-4503-0619-5

    Google Scholar 

  23. Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu. Y.: Learning fine-grained image similarity with deep ranking In: CVPR (2014)

    Google Scholar 

  24. Zeiler, M.D., Fergus, R.: Visualizing and Understanding Convolutional Networks (2013). arXiv preprint arXiv:1311.2901, http://arxiv.org/abs/1311.2901

  25. Zeiler, M.D., Fergus, R.: Stochastic pooling for regularization of deep convolutional neural networks (2013). arXiv preprint arXiv:1301.3557

Download references

Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan-Z GPU used for this research. This research was additionally supported by the Israel Science Foundation (ISF) grant No. 1271/13, and by the ISF-UGC India-Israel joint research program grant No. 1932/14.

Author information

Authors and Affiliations

  1. Department of Computer Science, Technion Israel Institute of Technology, Haifa, Israel

    Elad Hoffer & Nir Ailon

Authors
  1. Elad Hoffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nir Ailon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elad Hoffer .

Editor information

Editors and Affiliations

  1. University of Copenhagen, Copenhagen, Denmark

    Aasa Feragen

  2. DAIS, Università Ca' Foscari Venezia, Venezia Mestre, Italy

    Marcello Pelillo

  3. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Marco Loog

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Hoffer, E., Ailon, N. (2015). Deep Metric Learning Using Triplet Network. In: Feragen, A., Pelillo, M., Loog, M. (eds) Similarity-Based Pattern Recognition. SIMBAD 2015. Lecture Notes in Computer Science(), vol 9370. Springer, Cham. https://doi.org/10.1007/978-3-319-24261-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24261-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24260-6

  • Online ISBN: 978-3-319-24261-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation