1 |
HayesM P, GoughP T. Synthetic aperture sonar: a review of current status[J]. IEEE Journal of Oceanic Engineering, 2009, 34(3): 207 - 224.
|
2 |
WangP, ChiC, ZhangY, et al. Fast imaging algorithm for downward-looking 3D synthetic aperture sonars[J]. IET Radar, Sonar and Navigation, 2020, 14(3): 459 - 467.
|
3 |
刘纪元,唐劲松,孙宝申,等. 基于回波信号的一种合成孔径声纳运动补偿方法[J]. 电子学报, 2003, 31(1): 131 - 134.
|
|
LIUJ Y, TANGJ S, SUNB S, et al. A receiving-data-based motion compensation method of synthetic aperture sonar[J]. Acta Electronica Sinica, 2003, 31(1): 131 - 134. (in Chinese)
|
4 |
SunS B, ChenY C, QinL H, et al. Inverse synthetic aperture sonar imaging of underwater vehicles utilizing 3-D rotations[J]. IEEE Journal of Oceanic Engineering, 2020, 45(2): 563 - 576.
|
5 |
LiY, TangS, ZhangR, et al. Asymmetric GAN for unpaired image-to-image translation[J]. IEEE Transactions on Image Processing, 2019, 28(12): 5881 - 5896.
|
6 |
LinJ X, XiaY C, QinT, et al. Conditional image-to-image translation[A]. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition[C]. Salt Lake City, USA: Institute of Electrical and Electronics Engineering, 2018. 5524 - 5532.
|
7 |
HintonG. Where do features come from? [J]. Cognitive Science, 2014, 38(6):1078 - 1101.
|
8 |
LeCunY, BengioY, HintonG. Deep learning[J]. Nature, 2016, 521(7553): 436 - 444.
|
9 |
SchmidhuberJ. Deep learning in neural networks: an overview[J]. Neural Networks, 2015, 61:85 - 117.
|
10 |
贺昱曜, 李宝奇. 一种组合型的深度学习模型学习率策略[J]. 自动化学报, 2016, 42(6): 953 - 958.
|
|
HeY Y, LiB Q. A combinatory form learning rate scheduling for deep learning model[J]. Acta Automatica Sinica, 2016, 42(6): 953 - 958.(in Chinese)
|
11 |
GoodfellowI J, Pouget-AbadieJ, MirzaM, et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014, 3: 2672 - 2680.
|
12 |
RadfordA, MetzL. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[EB/OL]. , 2020-07-14.
|
13 |
ArjovskyM, ChintalaS, BottouL. Wasserstein GAN[EB/OL]. , 2020-07-14
|
14 |
ChenX, DuanY, HouthooftR, et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets[A]. Proceedings of the 2016 Advances in Neural Information Processing Systems [C]. Barcelona, Spain: Neural Information Processing Systems, 2016. 2172 - 2180.
|
15 |
XuQ T, HuangG, YuanY, et al. An Empirical Study on Evaluation Metrics of Generative Adversarial Networks[EB/OL]. , 2020-07-14.
|
16 |
IsolaP, ZhuJ Y, ZhouT, et al. Image⁃to⁃image translation with conditional adversarial networks[A]. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition[C]. Honolulu, HI, USA: Institute of Electrical and Electronics Engineering, 2017. 5967 - 5976.
|
17 |
ZhuJ Y, ParkT, IsolaP, et al. Unpaired image⁃to⁃image translation using cycle-consistent adversarial networks[A]. Proceedings of the 2017 IEEE International Conference on Computer Vision[C]. Venice, Italy: IEEE, 2017. 2242 - 2251.
|
18 |
HeK M, ZhangX Y, RenS Q, et al. Deep residual learning for image recognition[A]. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition[C]. Las Vegas, NV, USA: IEEE, 2016. 770 - 778.
|
19 |
XieS N, GirshickR, DollárP, et al. Aggregated residual transformations for deep neural networks[A]. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition[C]. Honolulu, HI, USA: IEEE,2017. 5987 - 5995.
|
20 |
SzegedyC, IoffeS, VanhouckeV, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[A]. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence[C]. San Francisco California USA: AAAI Press,2016. 4278 - 4284
|
21 |
LiX, WangW H, HuX L, et al. Selective kernel networks[A]. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition[C]. Long Beach, CA, USA: IEEE, 2019. 510 - 519.
|
22 |
HuangX J, WenL W, DingJ S. SAR and optical image registration method based on improved CycleGAN[A]. Proceedings of the 2019 Asia-Pacific Conference on Synthetic Aperture Radar[C]. Xiamen, China: IEEE, 2019. 1 - 6.
|
23 |
李宝奇, 贺昱曜, 强伟, 等. 基于并行附加特征提取网络的SSD地面小目标检测模型[J]. 电子学报, 2020, 48(1): 84 - 91.
|
|
LiB Q, HeY Y, QiangW, et al. SSD with parallel additional feature extraction network for ground small target detection[J]. Acta Electronica Sinica, 2020, 48(1) 84 - 91. (in Chinese)
|
24 |
HowardA G, ZhuM L, ChenB, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [EB/OL]. , 2020-07-14.
|
25 |
ChenL C, PapandreouG, KokkinosI, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834 - 848.
|
26 |
WangP Q, ChenP F, YuanY, et al. Understanding convolution for semantic segmentation[A]. Proceedings of the 2018 IEEE Conference on Applications of Computer Vision [C]. Lake Tahoe, NV: IEEE, 2018. 1451 - 1460.
|
27 |
WangZ, LiQ. Information content weighting for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1185 - 1198.
|
28 |
ChoiY, ChoiM, KimM, et al. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-image Translation[EB/OL]. , 2020-07-14.
|