中文

Journal of Shanghai University >

2023 , Vol. 29 >Issue 1: 1 - 9

DOI: https://doi.org/10.12066/j.issn.1007-2861.2460

Multimedia Information Processing

Robust metric transfer using joint adversarial training

Expand
  • 1. School of Computer Science, Wuhan University, Wuhan 430072, Hubei, China
    2. Hubei Luojia Laboratory, Wuhan 430079, Hubei, China
    3. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
    4. School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
    5. JD Explore Academy, Beijing 100191, China
    6. School of Computer Science, University of Sydney, New South Wales 2006, Australia

Received date: 2023-01-08

  Online published: 2023-03-28

Fold

Abstract

Transfer metric learning (TML) aims to improve the metric learning in target domains by transferring knowledge from those related tasks where the distance metrics are strong and reliable. Existing TML approaches focus on only transferring the source metric knowledge, which is often prone to overfitting to the source domain. In this study, we train a source metric that is appropriate for transfer and then design a general deep TML method for effective metric transfer. In particular, we propose learning the source metric parameterized by a deep neural network through joint adversarial training and then transferring the metric to the target domain by embedding imitation, which allows the inputs of source and target domains to be heterogeneous. Besides, we restrict the size of the target metric network to be small so that the inference is efficient in the target domain. Finally, the results of applying the proposed method to a popular face verification application demonstrate its effectiveness.

Key words： transfer metric learning (TML); deep metric learning; joint adversarial training; heterogeneous domain

Cite this article

YANG Qiancheng, LUO Yong, HU Han, ZHOU Xin, DU Bo, TAO Dacheng . Robust metric transfer using joint adversarial training[J]. Journal of Shanghai University, 2023 , 29(1) : 1 -9 . DOI: 10.12066/j.issn.1007-2861.2460

References

[1] Liu W Y, Wen Y D, Yu Z D, et al. SphereFace: deep hypersphere embedding for face recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017: 212-220.
[2] Brattoli B, Roth K, Ommer B. MIC: mining interclass characteristics for improved metric learning[C]// Proceedings of the International Conference on Computer Vision (ICCV). 2019: 7999-8008.
[3] Sanakoyeu A, Tschernezki V, Bü, et al. Divide and conquer the embedding space for metric learning[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019: 471-480.
[4] Ojala T, PietikÄINEN M, MÄENPÄÄ T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24: 971-987.
[5] Yang Q C, Luo Y, Hu H, et al. Robust metric boosts transfer[C]// IEEE 24th International Workshop on Multimedia Signal Processing (MMSP). 2022:1-6.
[6] Wang X, Han X, Huang W, et al. Multi-similarity loss with general pair weighting for deep metric learning[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019: 5022-5030.
[7] Xuan H, Stylianou A, Pless R. Improved embeddings with easy positive triplet mining[C]// IEEE Winter Conference on Applications of Computer Vision (WACV). 2020: 2463-2471
[8] Wang F, Liu W, Liu H, et al. Additive margin softmax for face verification[J]. IEEE Signal Processing Letters, 2018, 25: 926-930.
[9] Wen Y D, Zhang K P, Li Z F, et al. A discriminative feature learning approach for deep face recognition[C]// European Conference on Computer Vision (ECCV). 2016: 499-515.
[10] Guo Y, Shi H, Kumar A, et al. SpotTune: transfer learning through adaptive fine-tuning[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019: 4805-4814,
[11] Yu L, Yazici V O, Liu X L, et al. Learning metrics from teachers: compact networks for image embedding[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019: 2907-2916.
[12] Luo Y, Wen Y G, Liu T L, et al. Transferring knowledge fragments for learning distance metric from a heterogeneous domain[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41: 1013-1026.
[13] Wang C, Mahadevan S. Heterogeneous domain adaptation using manifold alignment[C]// Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). 2011: 1541-1546.
[14] Zhou J T, Tsang I W, Pan S J, et al. Heterogeneous domain adaptation for multiple classes[C]// Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS). 2014: 1095-1103.
[15] Ilyas A, Santurkar S, Tsipras D, et al. Adversarial examples are not bugs, they are features[C]// Proceedings of Advances in Neural Information Processing Systems (NIPS). 2019: 125-136.
[16] Utrera F, Kravitz E, Erichson N B, et al. Adversarially-trained deep nets transfer better: illustration on image classification[EB/OL]. (2020-07-11)[2023-01-10]. https://arxiv.org/abs/2007.05869.
[17] Qi G J, Aggarwal C C, Huang T S. Transfer learning of distance metrics by cross-domain metric sampling across heterogeneous spaces[C]// Proceedings of the 12th SIAM International Conference on Data Mining. 2012: 528-539.
[18] Zhang X, Zhou X, Lin M, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[EB/OL]. (2017-07-04)[2023-01-03]. https://arxiv.org/pdf/1707.01083.pdf.
[19] Goodfellow I J, Shlens J, Szegedy C. Explaining and harnessing adversarial examples[EB/OL]. (2014-12-20)[2023-01-03]. https://arxiv.org/pdf/1412.6572.pdf.
[20] Madry A, Makelov A, Schmidt L, et al. Towards deep learning models resistant to adversarial attacks[C]// Proceedings of 6th International Conference on Learning Representations (ICLR). 2018: 1-28.
[21] Huang G B, Ramesh M, Berg T, et al. Labeled faces in the wild: a database for studying face recognition in unconstrained environments[R/OL]. (2021-07-13) [2023-01-30] https://people.cs.umass.edu/~elm/papers/lfw.pdf.
[22] Dai D X, Kroeger T, Timofte L, et al. Metric imitation by manifold transfer for efficient vision applications[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015: 3527-3536.
Options
Outlines

/

〈 〉