Abstract
In many machine learning applications and algorithms, the algorithm performance and accuracy are highly dependent on the metric used to measure the distance between different samples. Therefore, learning a distance metric specific to the data can improve these algorithms’ performance. This paper proposes an unrestricted deep metric learning framework based on neural networks’ interaction for learning metrics in latent space. The proposed method is inspired by generative neural nets (GANs), in which two neural nets are working together to learn true data distribution. In our method, one network plays the role of a supervisor for another network, a feature learning auto-encoder. Its task is to learn transformation to latent space in which data have more meaningful distance and separability. i.e., the supervisor gets the output of the auto-encoder and sends feedback to modify its weights. They interact with each other interleavingly. Several experiments were conducted on four datasets, such as MNIST, GISETTE, Winnipeg Cropland Classification (WCC), and swarm behavior, from different application domains, to evaluate the proposed method’s performance. The results show that we can force auto-encoder to learn label information to project data into a latent space with better separability by using our approach. In addition to better class discrimination, the proposed method is far faster than normal auto-encoders during feature learning and has much less training time in the classification phase.
Similar content being viewed by others
References
Alwasiti H, Yusoff MZ, Raza K (2020) Motor imagery classification for brain computer interface using deep metric learning. IEEE Access 8:109949–109963
Ben X, Meng W, Yan R, Wang K (2012) An improved biometrics technique based on metric learning approach. Neurocomputing 97:44–51
Bromley J, Guyon I, LeCun Y, Säckinger E, Shah R (1994) Signature verification using a“ siamese” time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737–744
Cakir F, He K, Xia X, Kulis B, Sclaroff S (2019) Deep metric learning to rank. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1861–1870
Cao R, Zhang Q, Zhu J, Li Q, Li Q, Liu B, Qiu G (2020) Enhancing remote sensing image retrieval using a triplet deep metric learning network. Int J Remote Sens 41(2):740–751
Cheng G, Yang C, Yao X, Guo L, Han J (2018) When deep learning meets metric learning: remote sensing image scene classification via learning discriminative cnns. IEEE Trans Geosci Remote Sens 56(5):2811–2821
Davis JV, Kulis B, Jain P, Sra S, (2007) Dhillon I.S. Information-theoretic metric learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 209–216. ACM
Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml
Duan Y, Lu J, Feng J, Zhou J (2017) Deep localized metric learning. IEEE Trans Circuits Syst Video Technol 28(10):2644–2656
Elezi I, Vascon S, Torcinovich A, Pelillo M, Leal-Taixé L (2020) The group loss for deep metric learning. In: European Conference on Computer Vision, pp. 277–294. Springer
Faraki M, Harandi MT, Porikli F (2018) Large-scale metric learning: A voyage from shallow to deep. IEEE Trans Neural Netw Learn Syst 29(9):4339–4346
Ge W (2018) Deep metric learning with hierarchical triplet loss. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 269–285
Globerson A, Roweis ST (2006) Metric learning by collapsing classes. In: Advances in Neural Information Processing Systems, pp. 451–458
Goldberger J, Hinton GE, Roweis ST, Salakhutdinov RR (2005) Neighbourhood components analysis. In: Advances in Neural Information Processing Systems, pp. 513–520
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press . http://www.deeplearningbook.org
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680
Guyon I, Gunn S, Ben-Hur A, Dror G (2005) Result analysis of the nips 2003 feature selection challenge. In: L.K. Saul, Y. Weiss, L. Bottou (eds.) Advances in Neural Information Processing Systems 17, pp. 545–552. MIT Press
Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: International Workshop on Similarity-Based Pattern Recognition, pp. 84–92. Springer
Hu J, Lu J, Tan Y (2014) Discriminative deep metric learning for face verification in the wild. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875–1882
Jafarpisheh N, Teshnehlab M (2018) Cancers classification based on deep neural networks and emotional learning approach. IET Systems Biology
Khosravi I, Alavipanah SK (2019) A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations. Int J Remote Sens 40(18):7221–7251
Kim W, Goyal B, Chawla K, Lee J, Kwon K (2018) Attention-based ensemble for deep metric learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 736–751
Laradji IH, Babanezhad R (2020) M-adda: Unsupervised domain adaptation with deep metric learning. In: Domain Adaptation for Visual Understanding, pp. 17–31. Springer
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278–2324
Li X, Yin H, Zhou K, Zhou X (2020) Semi-supervised clustering with deep metric learning and graph embedding. World Wide Web 23(2):781–798
Liong VE, Lu J, Tan YP, Zhou J (2017) Deep coupled metric learning for cross-modal matching. IEEE Trans Multimedia 19(6):1234–1244
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Liu X, Kumar BV, You J, Jia P (2017) Adaptive deep metric learning for identity-aware facial expression recognition. In: CVPR Workshops, pp. 522–531
Lu J, Wang G, Deng W, Moulin P, Zhou J (2015) Multi-manifold deep metric learning for image set classification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1137–1145
McFee B, Barrington L, Lanckriet G (2012) Learning content similarity for music recommendation. IEEE Trans Audio, Speech, Lang Process 20(8):2207–2218
Qi GJ, Tang J, Zha ZJ, Chua TS, Zhang HJ (2009) An efficient sparse metric learning in high-dimensional space via l 1-penalized log-determinant regularization. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 841–848. ACM
Qian Q, Shang L, Sun B, Hu J, Li H, Jin R (2019) Softtriple loss: Deep metric learning without triplet sampling. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6450–6458
Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65
Salakhutdinov R, Hinton G (2007) Learning a nonlinear embedding by preserving class neighbourhood structure. In: Artificial Intelligence and Statistics, pp. 412–419
Schultz M, Joachims T (2004) Learning a distance metric from relative comparisons. In: Advances in Neural Information Processing Systems, pp. 41–48
Shaw B, Huang B, Jebara T (2011) Learning a distance metric from a network. In: Advances in Neural Information Processing Systems, pp. 1899–1907
Shental N, Hertz T, Weinshall D, Pavel M (2002) Adjustment learning and relevant component analysis. In: European Conference on Computer Vision, pp. 776–790. Springer
Song HO, Jegelka S, Rathod V, Murphy K (2016) Learnable structured clustering framework for deep metric learning. CoRR arXiv:1612.01213
Song HO, Jegelka S, Rathod V, Murphy K (2017) Deep metric learning via facility location. In: Computer Vision and Pattern Recognition (CVPR), vol. 8
Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10:207–244
Weinberger KQ, Tesauro G (2007) Metric learning for kernel regression. In: Artificial Intelligence and Statistics, pp. 612–619
Xing EP, Ng AY, Jordan MI, Russell S (2002) Distance metric learning, with application to clustering with side-information. In: Proceedings of the 15th International Conference on Neural Information Processing Systems, NIPS’02, pp. 521–528. MIT Press, Cambridge, MA, USA
Xu X, He L, Lu H, Gao L, Ji Y (2019) Deep adversarial metric learning for cross-modal retrieval. World Wide Web 22(2):657–672
Yu B, Tao D (2019) Deep metric learning with tuplet margin loss. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6490–6499
Zheng W, Chen Z, Lu J, Zhou J (2019) Hardness-aware deep metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 72–81
Zheng W, Lu J, Zhou J (2020) Deep metric learning via adaptive learnable assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2960–2969
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mehralian, S., Teshnehlab, M. & Nasersharif, B. Unrestricted deep metric learning using neural networks interaction. Pattern Anal Applic 24, 1699–1711 (2021). https://doi.org/10.1007/s10044-021-01018-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-021-01018-3