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|a 10.1109/TPAMI.2023.3338268
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|a eng
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|a Wang, Yikai
|e verfasserin
|4 aut
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|a Knockoffs-SPR
|b Clean Sample Selection in Learning With Noisy Labels
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|c 2024
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|a Date Revised 03.04.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR
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|a Journal Article
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|a Fu, Yanwei
|e verfasserin
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|a Sun, Xinwei
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|t IEEE transactions on pattern analysis and machine intelligence
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|g 46(2024), 5 vom: 03. Apr., Seite 3242-3256
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|u http://dx.doi.org/10.1109/TPAMI.2023.3338268
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