A Class Incremental Learning Algorithm with Dual Separation of Data Flow and Feature Space for Various Classes

YUN Tao; PAN Quan; LIU Lei; BAI Xianglong; LIU Hong

doi:10.11999/JEIT231064

Volume 46 Issue 10

Oct. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(10): 3879-3889

YUN Tao, PAN Quan, LIU Lei, BAI Xianglong, LIU Hong. A Class Incremental Learning Algorithm with Dual Separation of Data Flow and Feature Space for Various Classes[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3879-3889. doi: 10.11999/JEIT231064

Citation:

YUN Tao, PAN Quan, LIU Lei, BAI Xianglong, LIU Hong. A Class Incremental Learning Algorithm with Dual Separation of Data Flow and Feature Space for Various Classes[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3879-3889. doi: 10.11999/JEIT231064

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A Class Incremental Learning Algorithm with Dual Separation of Data Flow and Feature Space for Various Classes

doi: 10.11999/JEIT231064

YUN Tao^{1, 2},
PAN Quan^{1, 3
,
,},
LIU Lei⁴,
BAI Xianglong¹,
LIU Hong²

1.
School of Automation, Northwestern Polytechnical University, Xi’an 710114 China
2.
State Key Laboratory of Astronautic Dynamics, Xi’an 710043, China
3.
Key Laboratory of Information Fusion Technology of Ministry of Education, Xi’an 710114, China
4.
Xidian University, Xi’an 710071, China

Funds: The Major Project of the National Natural Science Foundation of China (61790552)

Received Date: 2023-10-07
Rev Recd Date: 2024-05-08

Available Online: 2024-06-16

Publish Date: 2024-10-30

Abstract

Abstract

To address the catastrophic forgetting problem in Class Incremental Learning (CIL), a class incremental learning algorithm with dual separation of data flow and feature space for various classes is proposed in this paper. The Dual Separation (S2) algorithm is composed of two stages in an incremental task. In the first stage, the network training is achieved through the comprehensive constraint of classification loss, distillation loss, and contrastive loss. The data flows from different classes are separated depending on module functions, in order to enhance the network’s ability to recognize new classes. By utilizing contrastive loss, the distance between different classes in the feature space is increased to prevent the feature space of old class from being eroded by the new class due to the incompleteness of the old class samples. In the second stage, the imbalanced dataset is subjected to dynamic balancing sampling to provide a balanced dataset for the new network’s dynamic fine-tuning. A high-resolution range profile incremental learning dataset of aircraft targets was created using observed and simulated data. The experimental results demonstrate that the algorithm proposed in this paper outperforms other algorithms in terms of overall performance and higher stability, while maintaining high plasticity.

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References(16)

References

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