Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask

Yuhang Zhang; Jie Geng; Wen Jiang

doi:10.1109/ICUS58632.2023.10318287

Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask

Yuhang Zhang, Jie Geng, Wen Jiang

电子信息学院

Northwestern Polytechnical University Xian

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Deep neural networks (DNN) have been widely used in SAR image Target recognition and have achieved great success. However, the black box attribute of DNN makes the mechanism of the network inexplicable and confusing. In this paper, a network layer analysis model based on generative adversarial mask (GAM) is proposed to enhance the structure interpretability of DNNs. Inspired by network adaptive search, we introduce a mask layer and construct a layer analysis model. After sparse training and generative adversarial training, the contribution of the network layer can be calculated based on the mask layer parameters. We conducted sufficient experiments on several typical DNNs to verify the effectiveness of GAM.

源语言	英语
主期刊名	Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
编辑	Rong Song
出版商	Institute of Electrical and Electronics Engineers Inc.
页	786-791
页数	6
ISBN（电子版）	9798350316308
DOI	https://doi.org/10.1109/ICUS58632.2023.10318287
出版状态	已出版 - 2023
活动	2023 IEEE International Conference on Unmanned Systems, ICUS 2023 - Hefei, 中国期限: 13 10月 2023 → 15 10月 2023

出版系列

姓名	Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

会议

会议	2023 IEEE International Conference on Unmanned Systems, ICUS 2023
国家/地区	中国
市	Hefei
时期	13/10/23 → 15/10/23

访问文件

10.1109/ICUS58632.2023.10318287

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引用此

Zhang, Y., Geng, J., & Jiang, W. (2023). Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask. 在 R. Song (编辑), Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023 (页码 786-791). (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS58632.2023.10318287

Zhang, Yuhang ; Geng, Jie ; Jiang, Wen. / Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask. Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. 编辑 / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 786-791 (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023).

@inproceedings{d7a5b575d8ed40e1b67fd520f06950fa,

title = "Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask",

abstract = "Deep neural networks (DNN) have been widely used in SAR image Target recognition and have achieved great success. However, the black box attribute of DNN makes the mechanism of the network inexplicable and confusing. In this paper, a network layer analysis model based on generative adversarial mask (GAM) is proposed to enhance the structure interpretability of DNNs. Inspired by network adaptive search, we introduce a mask layer and construct a layer analysis model. After sparse training and generative adversarial training, the contribution of the network layer can be calculated based on the mask layer parameters. We conducted sufficient experiments on several typical DNNs to verify the effectiveness of GAM.",

keywords = "Deep neural network, SAR image recognition, Structure interpretability",

author = "Yuhang Zhang and Jie Geng and Wen Jiang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Unmanned Systems, ICUS 2023 ; Conference date: 13-10-2023 Through 15-10-2023",

year = "2023",

doi = "10.1109/ICUS58632.2023.10318287",

language = "英语",

series = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "786--791",

editor = "Rong Song",

booktitle = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",

}

Zhang, Y, Geng, J & Jiang, W 2023, Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask. 在 R Song (编辑), Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023, Institute of Electrical and Electronics Engineers Inc., 页码 786-791, 2023 IEEE International Conference on Unmanned Systems, ICUS 2023, Hefei, 中国, 13/10/23. https://doi.org/10.1109/ICUS58632.2023.10318287

Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask. / Zhang, Yuhang; Geng, Jie ; Jiang, Wen.
Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. 编辑 / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 786-791 (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask

AU - Zhang, Yuhang

AU - Geng, Jie

AU - Jiang, Wen

PY - 2023

Y1 - 2023

N2 - Deep neural networks (DNN) have been widely used in SAR image Target recognition and have achieved great success. However, the black box attribute of DNN makes the mechanism of the network inexplicable and confusing. In this paper, a network layer analysis model based on generative adversarial mask (GAM) is proposed to enhance the structure interpretability of DNNs. Inspired by network adaptive search, we introduce a mask layer and construct a layer analysis model. After sparse training and generative adversarial training, the contribution of the network layer can be calculated based on the mask layer parameters. We conducted sufficient experiments on several typical DNNs to verify the effectiveness of GAM.

AB - Deep neural networks (DNN) have been widely used in SAR image Target recognition and have achieved great success. However, the black box attribute of DNN makes the mechanism of the network inexplicable and confusing. In this paper, a network layer analysis model based on generative adversarial mask (GAM) is proposed to enhance the structure interpretability of DNNs. Inspired by network adaptive search, we introduce a mask layer and construct a layer analysis model. After sparse training and generative adversarial training, the contribution of the network layer can be calculated based on the mask layer parameters. We conducted sufficient experiments on several typical DNNs to verify the effectiveness of GAM.

KW - Deep neural network

KW - SAR image recognition

KW - Structure interpretability

UR - http://www.scopus.com/inward/record.url?scp=85180127375&partnerID=8YFLogxK

U2 - 10.1109/ICUS58632.2023.10318287

DO - 10.1109/ICUS58632.2023.10318287

M3 - 会议稿件

AN - SCOPUS:85180127375

T3 - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

SP - 786

EP - 791

BT - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

A2 - Song, Rong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

Y2 - 13 October 2023 through 15 October 2023

ER -

Zhang Y, Geng J , Jiang W. Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask. 在 Song R, 编辑, Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. 页码 786-791. (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023). doi: 10.1109/ICUS58632.2023.10318287

Interpretability of Deep Network Structures for SAR Image Target Recognition Based on Generative Adversarial Mask

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