  Multi-Label Image Classification (MLIC) approaches usually exploit label
correlations to achieve good performance. However, emphasizing correlation like
co-occurrence may overlook discriminative features of the target itself and
lead to model overfitting, thus undermining the performance. In this study, we
propose a generic framework named Parallel Self-Distillation (PSD) for boosting
MLIC models. PSD decomposes the original MLIC task into several simpler MLIC
sub-tasks via two elaborated complementary task decomposition strategies named
Co-occurrence Graph Partition (CGP) and Dis-occurrence Graph Partition (DGP).
Then, the MLIC models of fewer categories are trained with these sub-tasks in
parallel for respectively learning the joint patterns and the category-specific
patterns of labels. Finally, knowledge distillation is leveraged to learn a
compact global ensemble of full categories with these learned patterns for
reconciling the label correlation exploitation and model overfitting. Extensive
results on MS-COCO and NUS-WIDE datasets demonstrate that our framework can be
easily plugged into many MLIC approaches and improve performances of recent
state-of-the-art approaches. The explainable visual study also further
validates that our method is able to learn both the category-specific and
co-occurring features. The source code is released at
https://github.com/Robbie-Xu/CPSD.
