Understanding Label Bias in Single Positive Multi-Label Learning
Julio Arroyo Pietro Perona Elijah Cole
Annotating data for multi-label classification is prohibitively expensive
because every category of interest must be confirmed to be present or absent.
Recent work on single positive multi-label (SPML) learning shows that it is
possible to train effective multi-label classifiers using only one positive
label per image. However, the standard benchmarks for SPML are derived from
traditional multi-label classification datasets by retaining one positive label
for each training example (chosen uniformly at random) and discarding all other
labels. In realistic settings it is not likely that positive labels are chosen
uniformly at random. This work introduces protocols for studying label bias in
SPML and provides new empirical results.