Deep Convolutional Neural Network Based on Two-Stream Convolutional Unit

Layer	LSNet^[14]	CTsNet
1	3×3×3×192	3×3×3×192
2	$\begin{matrix} [\begin{matrix} 3 \times 3 \times 1 \times 192 \\ 1 \times 1 \times 192 \times 192 \end{matrix}] \end{matrix}$	$\begin{matrix} [\begin{matrix} (3 \times 3 \times 1 \times 192), (1 \times 1 \times 192 \times 192) \\ 1 \times 1 \times (192 \times 2) \times 192 \end{matrix}] \end{matrix}$
3	$\begin{matrix} [\begin{matrix} 3 \times 3 \times 1 \times 192 \\ 1 \times 1 \times 192 \times 192 \end{matrix}] \end{matrix}$	$\begin{matrix} [\begin{matrix} (3 \times 3 \times 1 \times 192), (1 \times 1 \times 192 \times 192) \\ 1 \times 1 \times (192 \times 2) \times 192 \end{matrix}] \end{matrix}$
4	$\begin{matrix} [\begin{matrix} 3 \times 3 \times 1 \times 192 \\ 1 \times 1 \times 192 \times 192 \end{matrix}] \end{matrix}$	$\begin{matrix} [\begin{matrix} (3 \times 3 \times 1 \times 192), (1 \times 1 \times 192 \times 192) \\ 1 \times 1 \times (192 \times 2) \times 192 \end{matrix}] \end{matrix}$
5	3×3×192×256	3×3×192×192
6	$\begin{matrix} [\begin{matrix} 3 \times 3 \times 1 \times 256 \\ 1 \times 1 \times 256 \times 256 \end{matrix}] \end{matrix}$	$\begin{matrix} [\begin{matrix} (3 \times 3 \times 1 \times 192), (1 \times 1 \times 192 \times 192) \\ 1 \times 1 \times (192 \times 2) \times 192 \end{matrix}] \end{matrix}$
7	$\begin{matrix} [\begin{matrix} 3 \times 3 \times 1 \times 256 \\ 1 \times 1 \times 256 \times 256 \end{matrix}] \end{matrix}$	$\begin{matrix} [\begin{matrix} (3 \times 3 \times 1 \times 192), (1 \times 1 \times 192 \times 192) \\ 1 \times 1 \times (192 \times 2) \times 192 \end{matrix}] \end{matrix}$
8	$\begin{matrix} [\begin{matrix} 3 \times 3 \times 1 \times 256 \\ 1 \times 1 \times 256 \times 256 \end{matrix}] \end{matrix}$	$\begin{matrix} [\begin{matrix} (3 \times 3 \times 1 \times 192), (1 \times 1 \times 192 \times 192) \\ 1 \times 1 \times (192 \times 2) \times 192 \end{matrix}] \end{matrix}$
9	3×3×256×256	3×3×192×192
10	3×3×256×256	3×3×192×192
11	1×1×256×10	1×1×192×10

Table 2. Comparison on parameters and classification error rate of CTsNet and LSNet
View table
Table 2. Comparison on parameters and classification error rate of CTsNet and LSNet
Method Error /% Parameter /M
LSNet^[14] 6.86 1.95
CTsNet 6.33 1.67

Table 3. Classification error rate of CIFAR10 and CIFAR100 with various networks%

View table

Table 3. Classification error rate of CIFAR10 and CIFAR100 with various networks%

Method	CIFAR10	CIFAR10+	CIFAR100
Maxout^[18]	11.68	9.38	38.57
NIN^[13]	10.41	8.81	35.68
NIN+LA^[23]	9.59	7.51	34.40
FitNet^[21]	-	8.39	35.04
DSN^[24]	9.75	8.22	34.57
Highway^[25]	-	7.54	32.24
ALL-CNN^[19]	9.08	7.25	33.71
RCNN-160^[26]	8.69	7.10	31.75
ResNet-110^[12]	-	6.43	-
CSNet-M^[20]	8.15	6.38	30.24
CTsNet	7.95	6.33	30.12

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Congcong Hou, Yuqing He, Xiaoheng Jiang, Jing Pan. Deep Convolutional Neural Network Based on Two-Stream Convolutional Unit[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021005

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Paper Information

Category: Image processing

Received: Aug. 2, 2017

Accepted: --

Published Online: Sep. 10, 2018

The Author Email: Congcong Hou (houcc@tju.edu.cn)

DOI:10.3788/LOP55.021005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Configurations of network parameters

Table 1. Configurations of network parameters

Table 2. Comparison on parameters and classification error rate of CTsNet and LSNet

Table 2. Comparison on parameters and classification error rate of CTsNet and LSNet

Table 3. Classification error rate of CIFAR10 and CIFAR100 with various networks%

Table 3. Classification error rate of CIFAR10 and CIFAR100 with various networks%