Class-Incremental Lifelong Learning in Multi-Label Classification

• URL: http://arxiv.org/abs/2207.07840v1
• Date: Sat, 16 Jul 2022 05:14:07 GMT
• Title: Class-Incremental Lifelong Learning in Multi-Label Classification
• Authors: Kaile Du, Linyan Li, Fan Lyu, Fuyuan Hu, Zhenping Xia, Fenglei Xu
• Abstract summary: This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN) with a built Augmented Correlation Matrix (ACM) across sequential partial-label tasks.
• Score: 3.711485819097916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. Training on the data with Partial Labels in LML classification may result in more serious Catastrophic Forgetting in old classes. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN) with a built Augmented Correlation Matrix (ACM) across sequential partial-label tasks. The results of two benchmarks show that the method is effective for LML classification and reducing forgetting.

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