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

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.

Source: Journal of Innovative Optical Health Sciences - May 9, 2022 Category: Biomedical Science Authors: Yu Yao Kaiwen Xue Liwang Liu Shanshan Zhu Chengfeng Yue Yanhong Ji Source Type: research