  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.
