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论文:2023,Vol:41,Issue(2):303-309 |
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引用本文： |
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陈鑫哲, 梁红, 徐微雨. 基于声呐图像的类别增量学习方法研究[J]. 西北工业大学学报 |
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CHEN Xinzhe, LIANG Hong, XU Weiyu. Research on a class-incremental learning method based on sonar images[J]. Journal of Northwestern Polytechnical University |
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| 基于声呐图像的类别增量学习方法研究 |
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| 陈鑫哲, 梁红, 徐微雨 |
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| 西北工业大学 航海学院, 陕西 西安 710072 |
| 摘要: |
| 由于声呐图像分辨率低、样本数少,现有的类别增量学习网络对历史任务目标出现了严重的灾难性遗忘问题,导致所有任务目标的平均识别率降低。基于生成重放的框架模式,提出了一种改进的类别增量学习网络,设计搭建新的深层卷积生成对抗网络取代变分自编码器,作为生成重放增量网络的重构模型,提升图像的重构效果;构建新的卷积神经网络取代多层感知机,作为生成重放增量网络的识别网络,提升图像的分类识别性能。结果表明,改进的生成重放增量网络缓解了历史任务目标的灾难性遗忘问题,显著提高所有任务目标的平均识别率显著提高。 |
| 关键词:
声呐图像识别
生成重放
类别增量学习
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| Research on a class-incremental learning method based on sonar images |
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| CHEN Xinzhe, LIANG Hong, XU Weiyu |
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| School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| Due to the low resolution and the small number of samples of sonar images, the existing class incremental learning networks have a serious problem of catastrophic forgetting of historical task targets, resulting in a low average recognition rate of all task targets. Based on the framework model of generated replay, an improved class incremental learning network is proposed in this paper, and a new deep convolution generative adversarial network is designed and built to replace the variational autoencoder as the reconstruction model of generated replay incremental network to improve the effect of image reconstruction; a new convolution neural network is constructed to replace the multi-layer perception as the recognition network of generated replay incremental network to improve the performance of image classification and recognition. The results show that the improved generated replay incremental network alleviates the problem of catastrophic forgetting of historical task targets, and the average recognition rate for all task targets is significantly improved. |
| Key words:
sonar images recognition
generated replay
class-incremental learning
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| 收稿日期: 2022-06-29
修回日期:
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| DOI: 10.1051/jnwpu/20234120303 |
| 基金项目: 国家自然科学基金面上项目(61971354)资助 |
| 通讯作者: 梁红(1969-),西北工业大学教授,主要从事水下信号处理研究。e-mail:lianghong@nwpu.edu.cn
Email：lianghong@nwpu.edu.cn |
| 作者简介: 陈鑫哲(1999-),西北工业大学硕士研究生,主要从事水下目标识别研究。
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| 作者相关文章 |
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| 陈鑫哲 在本刊中的所有文章 |
| 梁红 在本刊中的所有文章 |
| 徐微雨 在本刊中的所有文章 |
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参考文献: |
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[1] LIU Y, SCHIELE B, SUN Q. Adaptive aggregation networks for class-incremental learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021:2544-2553
[2] 张智敏. 基于增量学习的分类算法研究[D]. 广州:华南理工大学,2010 ZHANG Zhimin. The study of classification based on incremental learning[D]. Guangzhou:South China University of Technology, 2010 (in Chinese)
[3] LI Z, HOIEM D. Learning without forgetting[J]. IEEE Trans on Pattern Analysis & Machine Intelligence, 2017, 40(12):2935-2947
[4] KIRKPATRICK J, PASCANU R, RABINOWITZ N, et al. Overcoming catastrophic forgetting in neural networks[J]. Proceedings of the National Academy of Sciences, 2017, 114(13):3521-3526
[5] SHIN H, LEE J K, KIM J, et al. Continual learning with deep generative replay[J]. Advances in Neural Information Processing Systems, 2017, 30:2994-3003
[6] 陈禹乐, 李博, 梁红, 等. 小样本下基于深度学习的声呐图像分类研究[J]. 西北工业大学学报, 2022, 40(4):739-745 CHEN Yule, LI Bo, LIANG Hong, et al. Research on sonar image few-shot classification based on deep learning[J]. Journal of Northwestern Polytechnical University, 2022, 40(4):739-745 (in Chinese)
[7] 盛子旗, 霍冠英. 样本仿真结合迁移学习的声呐图像水雷检测[J]. 智能系统学报, 2021, 16(2):385-392 SHENG Ziqi, HUO Guanying. Detection of underwater mine target in sidescan sonar image based on sample simulation and transfer learning[J]. CAAI Transactions on Intelligent Systems, 2021, 16(2):385-392 (in Chinese)
[8] KINGMA D P, WELLING M. Auto-encoding variational Bayes[C]//International Conference on Learning Representatio-ns, 2014
[9] TANG T, DENG C, HUANG G B. Extreme learning machine for multilayer perceptron[J]. IEEE Trans on Neural Networks and Leorning Systems, 2016, 27(4):809-821
[10] BOND-TAYLOR S, LEACH A, LONG Y, et al. Deep generative modelling:a comparative review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2022, 44(11):7327-7347
[11] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[C]//International Conference on Learning Representations, 2016
[12] LECUN Y, BENGIO Y, HINTON G E, et al. Deep learning[J]. Nature, 2015, 521(7553):436-444
[13] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90 |
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