Open set recognition algorithm based on Conditional Gaussian Encoder

Yan Tang; Zhijin Zhao; Chun Li; Xueyi Ye; Yan Tang; Zhijin Zhao; Chun Li; Xueyi Ye

doi:10.3934/mbe.2021328

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6620-6637. doi: 10.3934/mbe.2021328

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Open set recognition algorithm based on Conditional Gaussian Encoder

1.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China
2.
State Key Lab of Information Control Technology in Communication System, The 36th Research Institute of China Electronics Technology Group Corporation, Jiaxing, China

Received: 08 July 2021 Accepted: 01 August 2021 Published: 03 August 2021

For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network retains the original form of the signal as much as possible and deep neural network is used to extract useful information. CG-Encoder adopts residual network structure and a new Kullback-Leibler (KL) divergence is defined. In the training phase, the known classes are approximated to different Gaussian distributions in the latent space and the discrimination between classes is increased to improve the recognition performance of the known classes. In the testing phase, a specific and effective OSR algorithm flow is designed. Simulation experiments are carried out on 9 jamming types. The results show that the CSR and OSR performance of CG-Encoder is better than that of the other three kinds of network structures. When the openness is the maximum, the open set average accuracy of CG-Encoder is more than 70%, which is about 30% higher than the worst algorithm, and about 20% higher than the better one. When the openness is the minimum, the average accuracy of OSR is more than 95%.
- Jamming recognition,
- Open Set Recognition (OSR),
- Conditional Gaussian Encoder (CG-Encoder),
- residual network,
- Kullback-Leibler (KL) divergence
Citation: Yan Tang, Zhijin Zhao, Chun Li, Xueyi Ye. Open set recognition algorithm based on Conditional Gaussian Encoder[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6620-6637. doi: 10.3934/mbe.2021328

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Abstract

For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network retains the original form of the signal as much as possible and deep neural network is used to extract useful information. CG-Encoder adopts residual network structure and a new Kullback-Leibler (KL) divergence is defined. In the training phase, the known classes are approximated to different Gaussian distributions in the latent space and the discrimination between classes is increased to improve the recognition performance of the known classes. In the testing phase, a specific and effective OSR algorithm flow is designed. Simulation experiments are carried out on 9 jamming types. The results show that the CSR and OSR performance of CG-Encoder is better than that of the other three kinds of network structures. When the openness is the maximum, the open set average accuracy of CG-Encoder is more than 70%, which is about 30% higher than the worst algorithm, and about 20% higher than the better one. When the openness is the minimum, the average accuracy of OSR is more than 95%.

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