Multi-label learning in the presence of missing labels (MLML) is a
challenging problem. Existing methods mainly focus on the design of network
structures or training schemes, which increase the complexity of
implementation. This work seeks to fulfill the potential of loss function in
MLML without increasing the procedure and complexity. Toward this end, we
propose two simple yet effective methods via robust loss design based on an
observation that a model can identify missing labels during training with a
high precision. The first is a novel robust loss for negatives, namely the Hill
loss, which re-weights negatives in the shape of a hill to alleviate the effect
of false negatives. The second is a self-paced loss correction (SPLC) method,
which uses a loss derived from the maximum likelihood criterion under an
approximate distribution of missing labels. Comprehensive experiments on a vast
range of multi-label image classification datasets demonstrate that our methods
can remarkably boost the performance of MLML and achieve new state-of-the-art
loss functions in MLML.

该论文提出两种简单且有效的基于强大损失设计的方法，通过减少假负例的影响及使用基于缺失标签的最大似然标准来改进多标签学习的性能，从而在大量多标签图像分类数据集上实现了新的最先进的损失函数。