Adaptive Hierarchical Similarity Metric Learning with Noisy Labels
Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause a severe performance degrada...
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| Published in: | IEEE transactions on image processing Vol. 32; p. 1 |
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| Language: | English |
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01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause a severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML. In this paper, we propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, i.e ., class-wise divergence and sample-wise consistency. Specifically, class-wise divergence can effectively excavate richer similarity information beyond binary in modeling by taking advantage of Hyperbolic metric learning, while sample-wise consistency can further improve the generalization ability of the model using contrastive augmentation. More importantly, we design an adaptive strategy to integrate this information in a unified view. It is noteworthy that the new method can be extended to any pair-based metric loss. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current deep metric learning approaches. |
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| AbstractList | Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause a severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML. In this paper, we propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, i.e ., class-wise divergence and sample-wise consistency. Specifically, class-wise divergence can effectively excavate richer similarity information beyond binary in modeling by taking advantage of Hyperbolic metric learning, while sample-wise consistency can further improve the generalization ability of the model using contrastive augmentation. More importantly, we design an adaptive strategy to integrate this information in a unified view. It is noteworthy that the new method can be extended to any pair-based metric loss. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current deep metric learning approaches. Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause a severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML. In this paper, we propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, i.e., class-wise divergence and sample-wise consistency. Specifically, class-wise divergence can effectively excavate richer similarity information beyond binary in modeling by taking advantage of Hyperbolic metric learning, while sample-wise consistency can further improve the generalization ability of the model using contrastive augmentation. More importantly, we design an adaptive strategy to integrate this information in a unified view. It is noteworthy that the new method can be extended to any pair-based metric loss. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current deep metric learning approaches.Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause a severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML. In this paper, we propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, i.e., class-wise divergence and sample-wise consistency. Specifically, class-wise divergence can effectively excavate richer similarity information beyond binary in modeling by taking advantage of Hyperbolic metric learning, while sample-wise consistency can further improve the generalization ability of the model using contrastive augmentation. More importantly, we design an adaptive strategy to integrate this information in a unified view. It is noteworthy that the new method can be extended to any pair-based metric loss. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current deep metric learning approaches. |
| Author | Huang, Heng Deng, Cheng Yan, Jiexi Luo, Lei |
| Author_xml | – sequence: 1 givenname: Jiexi surname: Yan fullname: Yan, Jiexi organization: School of Computer Science and Technology, Xidian University, Xi'an, China – sequence: 2 givenname: Lei surname: Luo fullname: Luo, Lei organization: JD Finance American Corporation, Mountain View, CA, USA – sequence: 3 givenname: Cheng orcidid: 0000-0003-2620-3247 surname: Deng fullname: Deng, Cheng organization: School of Electronic Engineering, Xidian University, Xi'an, China – sequence: 4 givenname: Heng orcidid: 0000-0002-3483-8333 surname: Huang fullname: Huang, Heng organization: Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37022798$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adaptation models Cognitive tasks Consistency Contrastive Augmentation Deep Metric Learning Geometry Hierarchical Similarity Hyperbolic Geometry Labels Machine learning Measurement Noise measurement Noisy Labels Performance degradation Robustness Similarity Task analysis Training |
| Title | Adaptive Hierarchical Similarity Metric Learning with Noisy Labels |
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