Towards Imbalanced Large Scale Multi-label Classification with Partially
  Annotated Labels
XIn Zhang Yuqi Song Fei Zuo Xiaofeng Wang
Multi-label classification is a widely encountered problem in daily life,
where an instance can be associated with multiple classes. In theory, this is a
supervised learning method that requires a large amount of labeling. However,
annotating data is time-consuming and may be infeasible for huge labeling
spaces. In addition, label imbalance can limit the performance of multi-label
classifiers, especially when some labels are missing. Therefore, it is
meaningful to study how to train neural networks using partial labels. In this
work, we address the issue of label imbalance and investigate how to train
classifiers using partial labels in large labeling spaces. First, we introduce
the pseudo-labeling technique, which allows commonly adopted networks to be
applied in partially labeled settings without the need for additional complex
structures. Then, we propose a novel loss function that leverages statistical
information from existing datasets to effectively alleviate the label imbalance
problem. In addition, we design a dynamic training scheme to reduce the
dimension of the labeling space and further mitigate the imbalance. Finally, we
conduct extensive experiments on some publicly available multi-label datasets
such as COCO, NUS-WIDE, CUB, and Open Images to demonstrate the effectiveness
of the proposed approach. The results show that our approach outperforms
several state-of-the-art methods, and surprisingly, in some partial labeling
settings, our approach even exceeds the methods trained with full labels.