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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Vydané v:	IEEE transactions on image processing Ročník 32; s. 1
Hlavní autori:	Yan, Jiexi, Luo, Lei, Deng, Cheng, Huang, Heng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line prístup:	Získať plný text
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