Class-Incremental Lifelong Learning in Multi-Label Classification
This addresses incremental learning for multi-label data, a domain-specific extension of existing single-label methods.
The paper tackles the problem of catastrophic forgetting in lifelong multi-label classification by proposing an Augmented Graph Convolutional Network with an Augmented Correlation Matrix, showing effectiveness on two benchmarks.
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.