1 |
FUKUSHIMA K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J]. Biological Cybernetics, 1980, 36(4): 193-202.
|
2 |
LECUN Y, BOTTOU L. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
|
3 |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems-Volume 1. Lake Tahoe: MIT Press, 2012: 1106-1114.
|
4 |
TAJBAKHSH N, SHIN J Y, GURUDU S R, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning?[J]. IEEE Transactions on Medical Imaging, 2016, 35(5): 1299-1312.
|
5 |
SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 1-9.
|
6 |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04)[2022-08-05]. .
|
7 |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
|
8 |
IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning. Lille, France: ACM, 2015: 448-456.
|
9 |
HOWARD A G, ZHU M, CHEN B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17)[2022-08-05]. .
|
10 |
ZHANG X, ZHOU X, LIN M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 6848-6856.
|
11 |
GOMES R, ROZARIO P, ADHIKARI N. Deep learning optimization in remote sensing image segmentation using dilated convolutions and ShuffleNet[C]//2021 IEEE International Conference on Electro Information Technology(EIT). Mt Pleasant, MI, USA: IEEE, 2021: 244-249.
|
12 |
TAN M, LE Q. Efficientnet: Rethinking model scaling for convolutional neural networks[C]//International Conference on Machine Learning. Long Beach, California, USA: ACM, 2019: 6105-6114.
|
13 |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 7132-7141.
|
14 |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2022-08-05]. .
|
15 |
SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Thirty-first AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI Press, 2017: 4278-4284.
|
16 |
KAISER L, GOMEZ A N, CHOLLET F. Depthwise separable convolutions for neural machine translation[EB/OL]. (2017-06-09)[2022-08-05]. .
|
17 |
伍邦谷, 张苏林, 石红, 朱鹏飞, 王旗龙, 胡清华. 基于多分支结构的不确定性局部通道注意力机制[J]. 电子学报, 2022, 50(2): 374-382.
|
|
WU Bang-gu, ZHANG Su-lin, SHI Hong, ZHU Peng-fei, WANG Qi-long, HU Qing-hua. Multi-branch structure based local channel attention with uncertainty[J]. Acta Electronica Sinica, 2022, 50(2): 374-382. (in Chinese)
|
18 |
TAFAZZOLI F, FRIGUI H, NISHIYAMA K. A large and diverse dataset for improved vehicle make and model recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 874-881.
|
19 |
TAN M, LE Q. Efficientnetv2: Smaller models and faster training[EB/OL]. (2021-04-01)[2022-08-05]. .
|
20 |
SANDLER M, HOWARD A, ZHU M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 4510-4520.
|
21 |
孟琭, 徐磊, 郭嘉阳. 一种基于改进的MobileNetV2网络语义分割算法[J]. 电子学报, 2020, 48(9): 1769-1776.
|
|
MENG Lu, XU Lei, GUO Jia-yang. Semantic segmentation algorithm based on improved MobileNetV2[J]. Acta Electronica Sinica, 2020, 48(9): 1769-1776. (in Chinese)
|
22 |
MA N, ZHANG X, ZHENG H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//European Conference on Computer Vision. Munich, Germany: Springer, 2018: 122-138.
|