An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels

Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically...
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Autor*in:	Xueqing Tian [verfasserIn] Dongyang Hou [verfasserIn] Siyuan Wang [verfasserIn] Xuanyou Liu [verfasserIn] Huaqiao Xing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	noisy labels robust loss remote sensing image retrieval deep learning

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 14(2024), 5, p 1756
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:5, p 1756

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app14051756

Katalog-ID:	DOAJ091278473

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language	English
source	In Applied Sciences 14(2024), 5, p 1756 volume:14 year:2024 number:5, p 1756
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topic_facet	noisy labels robust loss remote sensing image retrieval deep learning Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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authorswithroles_txt_mv	Xueqing Tian @@aut@@ Dongyang Hou @@aut@@ Siyuan Wang @@aut@@ Xuanyou Liu @@aut@@ Huaqiao Xing @@aut@@
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callnumber-first	T - Technology
author	Xueqing Tian
spellingShingle	Xueqing Tian misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc noisy labels misc robust loss misc remote sensing image retrieval misc deep learning misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels noisy labels robust loss remote sensing image retrieval deep learning
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc noisy labels misc robust loss misc remote sensing image retrieval misc deep learning misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
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title_sort	adaptive weighted method for remote sensing image retrieval with noisy labels
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title_auth	An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
abstract	Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.
abstractGer	Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.
abstract_unstemmed	Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.
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title_short	An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
url	https://doi.org/10.3390/app14051756 https://doaj.org/article/4509754669a34b55959c4cce6242c25a https://www.mdpi.com/2076-3417/14/5/1756 https://doaj.org/toc/2076-3417
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An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels

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