Multi-Label Continual Learning (MLCL) builds a class-incremental framework in
a sequential multi-label image recognition data stream. The critical challenges
of MLCL are the construction of label relationships on past-missing and
future-missing partial labels of training data and the catastrophic forgetting
on old classes, resulting in poor generalization. To solve the problems, the
study proposes an Augmented Graph Convolutional Network (AGCN++) that can
construct the cross-task label relationships in MLCL and sustain catastrophic
forgetting. First, we build an Augmented Correlation Matrix (ACM) across all
seen classes, where the intra-task relationships derive from the hard label
statistics. In contrast, the inter-task relationships leverage hard and soft
labels from data and a constructed expert network. Then, we propose a novel
partial label encoder (PLE) for MLCL, which can extract dynamic class
representation for each partial label image as graph nodes and help generate
soft labels to create a more convincing ACM and suppress forgetting. Last, to
suppress the forgetting of label dependencies across old tasks, we propose a
relationship-preserving constrainter to construct label relationships. The
inter-class topology can be augmented automatically, which also yields
effective class representations. The proposed method is evaluated using two
multi-label image benchmarks. The experimental results show that the proposed
way is effective for MLCL image recognition and can build convincing
correlations across tasks even if the labels of previous tasks are missing.

提出增强图卷积网络（AGCN++）并使用部分标签编码器（PLE）对多标签连续学习（MLCL）进行跨任务标签关系构建、避免灾难性遗忘等处理，实验结果表明该方法是有效的。

使用增强图卷积网络的多标签持续学习

Multi-Label Continual Learning using Augmented Graph Convolutional Network

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.

研究 Lifelong Multi-Label 分类，提出了一种面向序列多标签分类数据流的在线类增量分类器，使用增强图卷积网络和内置增强相关矩阵对序列部分标签任务进行训练，结果表明该方法对于 LML 分类和减少遗忘的有效性。

多标签分类中的增量式生命周期学习

Class-Incremental Lifelong Learning in Multi-Label Classification

The Lifelong Multi-Label (LML) image recognition builds an online
class-incremental classifier in a sequential multi-label image recognition data
stream. The key challenges of LML image recognition are the construction of
label relationships on Partial Labels of training data and the Catastrophic
Forgetting on old classes, resulting in poor generalization. To solve the
problems, the study proposes an Augmented Graph Convolutional Network (AGCN)
model that can construct the label relationships across the sequential
recognition tasks and sustain the catastrophic forgetting. First, we build an
Augmented Correlation Matrix (ACM) across all seen classes, where the
intra-task relationships derive from the hard label statistics while the
inter-task relationships leverage both hard and soft labels from data and a
constructed expert network. Then, based on the ACM, the proposed AGCN captures
label dependencies with dynamic augmented structure and yields effective class
representations. Last, to suppress the forgetting of label dependencies across
old tasks, we propose a relationship-preserving loss as a constraint to the
construction of label relationships. The proposed method is evaluated using two
multi-label image benchmarks and the experimental results show that the
proposed method is effective for LML image recognition and can build convincing
correlation across tasks even if the labels of previous tasks are missing. Our
code is available at this https URL.

此研究提出了增强图卷积网络（AGCN）模型来构建标签关系和保持关系跨多个图像识别任务，有效抑制遗忘效应，实验证明该方法在 LML 图像识别上效果可靠。