.png){kind=icon}
返回
Semi-supervised target classification in multi-frequency echosounder data
.png){kind=icon}
OA
AI
DOI：10.1093/icesjms/fsab140.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem of current methods is the heavy dependence on the manual categorization of data samples. Asa solution, we propose a novel semi-supervised deep learning method leveraging a few annotated data samples together with vast amounts of unannotated data samples, all in a single model. Specifically, two inter-connected objectives, namely, a clustering objective and a classification objective, optimize one shared convolutional neural network in an alternating manner. The clustering objective exploits the underlying structure of all data, both annotated and unannotated; the classification objective enforces a certain consistency to given classes using the few annotated data samples. We evaluate our classification method using echosounder data from the sandeel case study in the North Sea. In the semi-supervised setting with only a tenth of the training data annotated, our method achieves 67.6% accuracy, outperforming a conventional semi-supervised method by 7.0 percentage points. When applying the proposed method in a fully supervised setup, we achieve 74.7% accuracy, surpassing the standard supervised deep learning method by 4.7 percentage points.
Keyword:
acoustic target classification
deep clustering
limited annotation
pseudo-labeling
semi-supervised deep learning
期刊
IF:
3.4
论文数:
6.3K
被引数:
1.4W
机构
引用论文
A systematic study of the class imbalance problem in convolutional neural networks卷积神经网络中类不平衡问题的系统研究.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏The Vapor Phase Radiolysis of Isopropanol and Water–Isopropanol Mixtures. II. Nitrous Oxide as Electron Scavenger异丙醇和水的气相辐解。II. 以氧化亚氮为电子捕获剂
PREAI
分享收藏Economic benefit evaluation method for the micro-grid renewable energy system operation微网可再生能源系统运行经济效益评价方法
PREAI
分享收藏分享收藏分享收藏
查看更多内容