  Compared with multi-class classification, multi-label classification that
contains more than one class is more suitable in real life scenarios. Obtaining
fully labeled high-quality datasets for multi-label classification problems,
however, is extremely expensive, and sometimes even infeasible, with respect to
annotation efforts, especially when the label spaces are too large. This
motivates the research on partial-label classification, where only a limited
number of labels are annotated and the others are missing. To address this
problem, we first propose a pseudo-label based approach to reduce the cost of
annotation without bringing additional complexity to the existing
classification networks. Then we quantitatively study the impact of missing
labels on the performance of classifier. Furthermore, by designing a novel loss
function, we are able to relax the requirement that each instance must contain
at least one positive label, which is commonly used in most existing
approaches. Through comprehensive experiments on three large-scale multi-label
image datasets, i.e. MS-COCO, NUS-WIDE, and Pascal VOC12, we show that our
method can handle the imbalance between positive labels and negative labels,
while still outperforming existing missing-label learning approaches in most
cases, and in some cases even approaches with fully labeled datasets.
