融合注意力和胶囊池化的轻量型胶囊网络

doi:10.16180/j.cnki.issn1007-7820.2024.05.001

Abstract

Abstract:

In view of the inefficiency of feature information propagation in capsule networks and the huge computational overhead in the routing process, a graph pooling capsule network that combines attention and capsule pooling is proposed. The network mainly has the following two advantages: 1) The capsule attention is proposed, and the attention is applied to the primary capsule layer, which enhances the attention to the important capsules, and improves the accuracy of the prediction of the lower capsules to the higher capsules; 2) A new capsule pooling is proposed. The capsule with the largest weight is screened out at the corresponding positions of all feature maps in the primary capsule layer, and the effective feature information is represented by a small number of important capsules while reducing the number of model parameters. Results on public data sets show that the proposed capsule network achieves the accuracy of 92.60% on CIFAR10 and has excellent robustness against white-box adversarial attacks on complex datasets. In addition, the proposed capsule network achieves 95.74% accuracy on the AffNIST data set with superior affine transformation robustness. The calculation efficiency results show that the amount of floating-point operations of the proposed capsule is reduced by 31.3% and the number of parameters is reduced by 41.9% when compared with traditional CapsNet.

Key words: deep learning, image classification, capsule network, capsule pooling, attention mechanism, robustness, adversarial attack, lightweight

CLC Number:

TP391.4

ZHU Zihao, SONG Yan. Lightweight Capsule Network Fusing Attention and Capsule Pooling[J].Electronic Science and Technology, 2024, 37(5): 1-8.

Figures/Tables 10

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References 21

[1]	Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-232.
[2]	刘林嵩, 仝明磊, 吴东亮. SA-CapsNet:自注意力胶囊网络[J]. 计算机应用研究, 2021, 38(10):3005-3008,3039.
	Liu Linsong, Gong Minglei, Wu Dongliang. SA-CapsNet: Self-attention capsule network[J]. Application Research of Computers, 2021, 38(10):3005-3008,3039.
[3]	张伟, 刘娜, 江洋, 等. 基于YOLO神经网络的垃圾检测与分类[J]. 电子科技, 2022, 35(10):45-50.
	Zhang Wei, Liu Na, Jiang Yang, et al. Garbage detection and classification based on YOLO neural network[J]. Electronic Science and Technology, 2022, 35(10):45-50.
[4]	房巾莉, 吕毅斌, 王樱子, 等. 基于水平集的医学图像分割算法[J]. 电子科技, 2021, 34(2):12-20.
	Fang Jinli, Lü Yibin, Wang Yingzi, et al. A novel medical image segmentation algorithm based on level set[J]. Electronic Science and Technology, 2021, 34(2):12-20.
[5]	李业良, 张二华, 唐振民. 基于混合式注意力机制的语音识别研究[J]. 计算机应用研究, 2020, 37(1): 131-134.
	Li Yeliang, Zhang Erhua, Tang Zhenmin. Research on speech recognition based on hybrid attention mechanism[J]. Application Research of Computers, 2020, 37(1):131-134.
[6]	Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]. Long Beach: Proceedings of Neural Information Processing Systems, NeurIPS, 2017:3856-3866.
[7]	Xiang C, Zhang L, Tang Y, et al. MS-CapsNet: A novel multiscale capsule network[J]. IEEE Signal Processing Letters, 2018, 25(12):1850-1854.
[8]	Rajasegaran J, Jayasundara V, Jayasekara S, et al. Deepcaps: Going deeper with capsule networks[C]. Los Angeles: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:10725-10733.
[9]	Gu J D. Interpretable graph capsule networks for object recognition[C]. Vancouver: Proceedings of the AAAI Conference on Artificial Intelligence, 2021:1469-1477.
[10]	Huang R Y, Li J P, Wang S H, et al. A robust weight-shared capsule network for intelligent machinery fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(10):6466-6475.
[11]	Jia X F, Li J Q, Zhao B T, et al. Res-CapsNet:Residual capsule network for data classification[J]. Neural Process Letter, 2022, 54(5):4229-4245.
[12]	He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]. Seattle: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:770-778.
[13]	Choi J, Seo H, Im S, et al. Attention routing between capsules[C]. Seoul: IEEE/CVF International Conference on Computer Vision Workshops, 2019:1981-1989.
[14]	Hinton G E, Sabour S, Frosst N. Matrix capsules with EM routing[C]. Vancouver: International Conference on Learning Representations, 2018:1-15.
[15]	Ribeiro F D S, Leontidis G, Kollias S. Capsule routing via variational bayes[C]. New York: AAAI Conference on Artificial Intelligence, 2020:3749-3756.
[16]	Hahn T, Pyeon M, Kim G. Self-routing capsule networks[C]. Vancouver: Advances in Neural Information Processing Systems, 2019:7658-7667.
[17]	Gu J D, Tresp V. Improving the robustness of capsule networks to image affine transformations[C]. Seattle: IEEE Conference on Computer Vision and Pattern Recognition, 2020:7283-7291.
[18]	Zhang S F, Zhao W, Wu X F, et al. Fast dynamic routing based on weighted kernel density estimation[C]. Daegu: International Symposium on Artificial Intelligence and Robotics, 2021: 5281-5297.
[19]	Jeong T, Lee Y, Kim H. Ladder capsule network[C]. New York: International Conference on Machine Learning, 2019:3071-3079.
[20]	Lenssen J E, Fey M, Libuschewski P. Group equivariant capsule networks[C]. Montréal: Advances in Neural Information Processing Systems, 2018:8844-8853.
[21]	Kosiorek A R, Sabour S, Teh Y W, et al. Stacked capsule autoencoders[C]. Vancouver: Advances in Neural Information Processing Systems, 2019:15486-15496.

模型方法	MNIST		FashionMNIST		SVHN		CIFAR10		SmallNorb
模型方法	错误率/%	参数量/MB	错误率/%	参数量/MB	错误率/%	参数量/MB	错误率/%	参数量/MB	错误率/%	参数量/MB
Attn-Routing^[13]	0.54	5.31	-	-	-	-	12.71	9.600	-	-
EM-Routing^[14]	-	-	-	-	-	-	11.90	0.450	1.80	0.30
VB-Routing^[15]	-	-	5.20	0.17	3.90	0.320	11.20	0.320	1.60	0.17
Self-routing^[16]	-	-	-	-	3.12	140.300	7.86	140.300	-	-
DRCaps^[6]	0.32	6.10	5.45	6.10	3.85	6.100	9.94	6.100	3.52	6.10
Aff-CapsNet^[17]	0.46	8.20	7.47	8.20	7.85	8.200	23.72	8.200	-	-
MS-CapsNet^[7]	-	-	7.30	10.80	-	-	24.30	11.200	-	-
FRMS^[18]	0.42	1.2	6.00	1.20			15.60	1.200
GraCapsnets^[9]	-	-	-	-	2.98	2.475	7.99	2.475	-	-
CAPCaps(本文)	0.29	3.00	5.41	3.00	3.46	3.540	7.40	3.540	4.90	3.54

模型	CIFAR10/%	SVHN/%
Baseline	9.20	4.09
Baseline+(CA)	8.86	3.70
Baseline+(CP)	8.90	3.77
Baseline+(CA+CP)	7.40	3.46

模型方法	MNIST分类精度/%	AffNIST分类精度/%
CNN^[14]	99.20	85.90
Dynamic^[6]	99.20	79.00
LadderCaps^[19]	99.30	87.80
GCaps^[20]	98.40	89.10
Attn-Routing^[13]	99.50	91.60
SCAE^[21]	98.50	92.20
Aff-CapsNet^[17]	99.23	93.12
EM-Routing^[14]	99.20	93.10
DRCaps	99.20	95.42
CAPCaps	99.20	93.25
CAPCaps	99.56	95.74

模型方法	MNIST测试精度/%	Rotate-MNIST测试精度/%
AvgPool	99.48	95.73
Conv	99.47	96.49
DRCaps	99.50	97.25
CAPCaps	99.49	97.92

模型方法	Params/MB	Flops/MFlops
DRCaps	6.100	552.38
GraCapsnets	2.475	380.00
CAPCaps	3.540	380.00

Lightweight Capsule Network Fusing Attention and Capsule Pooling

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