  Due to the expensive costs of collecting labels in multi-label classification
datasets, partially annotated multi-label classification has become an emerging
field in computer vision. One baseline approach to this task is to assume
unobserved labels as negative labels, but this assumption induces label noise
as a form of false negative. To understand the negative impact caused by false
negative labels, we study how these labels affect the model's explanation. We
observe that the explanation of two models, trained with full and partial
labels each, highlights similar regions but with different scaling, where the
latter tends to have lower attribution scores. Based on these findings, we
propose to boost the attribution scores of the model trained with partial
labels to make its explanation resemble that of the model trained with full
labels. Even with the conceptually simple approach, the multi-label
classification performance improves by a large margin in three different
datasets on a single positive label setting and one on a large-scale partial
label setting. Code is available at
https://github.com/youngwk/BridgeGapExplanationPAMC.
