Corrections to “CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection”

In the above article [1], in (19), the original formulation has been revised and replaced with the following: \begin{equation*} \textbf {R}_{t}\gets \ \textbf {R}_{t-1}\cup \ k\left ({{\textbf B_{t}}}\right ),\textbf {R}_{0}=\phi . {5pt}\tag {19}\end{equation*}

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3582103
Citations
1
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.81
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article

Corrections to “CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection”

Jianing Wang, Maoguo Gong, Yuqiong Yao, Wan Zhang et al.
1 citations
IEEE Transactions on Geoscience and Remote Sensing
Machine Learning in Bioinformatics
0.81
article

Corrections to “CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection”

Jianing Wang, Maoguo Gong, Yuqiong Yao, Wan Zhang, Hua Zheng, Shengjia Hao
article en
1 citations

Abstract

In the above article [1], in (19), the original formulation has been revised and replaced with the following: \begin{equation*} \textbf {R}_{t}\gets \ \textbf {R}_{t-1}\cup \ k\left ({{\textbf B_{t}}}\right ),\textbf {R}_{0}=\phi . {5pt}\tag {19}\end{equation*}

IEEE Transactions on Geoscience and Remote SensingVol. 63
Xidian University (CN)
Openalex Percentile: Top 25%
Machine Learning in Bioinformatics
0.81
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