Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model

Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology. However, almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth, and may not be well generalized to unseen datasets, which...
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Autor*in:	Yu Yao [verfasserIn] Kaiwen Xue [verfasserIn] Liwang Liu [verfasserIn] Shanshan Zhu [verfasserIn] Chengfeng Yue [verfasserIn] Yanhong Ji [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Domain adaption suspension arrays deep learning Raman spectrum generalization

Übergeordnetes Werk:	In: Journal of Innovative Optical Health Sciences - World Scientific Publishing, 2017, 15(2022), 04
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:04

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1142/S1793545822500250

Katalog-ID:	DOAJ079168078

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However, almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth, and may not be well generalized to unseen datasets, which were collected under different experimental conditions, applying with the same coded material. In this study, we propose an improved model based on CyCADA, named as Detail constraint Cycle Domain Adaptive Model (DCDA). DCDA implements the classification of unseen datasets through domain adaptation, adapts representations at the encode level with decoder-share, and enforces coding features while leveraging a feat loss. To improve detailed structural constraints, DCDA takes downsample connection and skips connection. Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets. Compared with other models, extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization. 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callnumber-first	Q - Science
author	Yu Yao
spellingShingle	Yu Yao misc QC350-467 misc Domain adaption misc suspension arrays misc deep learning misc Raman spectrum misc generalization misc Technology misc T misc Optics. Light Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model
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topic_title	QC350-467 Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model Domain adaption suspension arrays deep learning Raman spectrum generalization
topic	misc QC350-467 misc Domain adaption misc suspension arrays misc deep learning misc Raman spectrum misc generalization misc Technology misc T misc Optics. Light
topic_unstemmed	misc QC350-467 misc Domain adaption misc suspension arrays misc deep learning misc Raman spectrum misc generalization misc Technology misc T misc Optics. Light
topic_browse	misc QC350-467 misc Domain adaption misc suspension arrays misc deep learning misc Raman spectrum misc generalization misc Technology misc T misc Optics. Light
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title	Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model
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title_full	Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model
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title_sort	decoding of raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model
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title_auth	Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model
abstract	Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology. However, almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth, and may not be well generalized to unseen datasets, which were collected under different experimental conditions, applying with the same coded material. In this study, we propose an improved model based on CyCADA, named as Detail constraint Cycle Domain Adaptive Model (DCDA). DCDA implements the classification of unseen datasets through domain adaptation, adapts representations at the encode level with decoder-share, and enforces coding features while leveraging a feat loss. To improve detailed structural constraints, DCDA takes downsample connection and skips connection. Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets. Compared with other models, extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization. The model proposed by the research achieves a classification with an accuracy of 100% when applied in datasets, in which the spectrum in the source domain is far less than the target domain.
abstractGer	Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology. However, almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth, and may not be well generalized to unseen datasets, which were collected under different experimental conditions, applying with the same coded material. In this study, we propose an improved model based on CyCADA, named as Detail constraint Cycle Domain Adaptive Model (DCDA). DCDA implements the classification of unseen datasets through domain adaptation, adapts representations at the encode level with decoder-share, and enforces coding features while leveraging a feat loss. To improve detailed structural constraints, DCDA takes downsample connection and skips connection. Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets. Compared with other models, extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization. The model proposed by the research achieves a classification with an accuracy of 100% when applied in datasets, in which the spectrum in the source domain is far less than the target domain.
abstract_unstemmed	Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology. However, almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth, and may not be well generalized to unseen datasets, which were collected under different experimental conditions, applying with the same coded material. In this study, we propose an improved model based on CyCADA, named as Detail constraint Cycle Domain Adaptive Model (DCDA). DCDA implements the classification of unseen datasets through domain adaptation, adapts representations at the encode level with decoder-share, and enforces coding features while leveraging a feat loss. To improve detailed structural constraints, DCDA takes downsample connection and skips connection. Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets. Compared with other models, extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization. The model proposed by the research achieves a classification with an accuracy of 100% when applied in datasets, in which the spectrum in the source domain is far less than the target domain.
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title_short	Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model
url	https://doi.org/10.1142/S1793545822500250 https://doaj.org/article/bb2234f8ac5f4d4592c433baed65fc5c https://www.worldscientific.com/doi/10.1142/S1793545822500250 https://doaj.org/toc/1793-5458 https://doaj.org/toc/1793-7205
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up_date	2024-07-03T22:04:18.376Z
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Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model

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