  Weak-label learning is a challenging task that requires learning from data
"bags" containing positive and negative instances, but only the bag labels are
known. The pool of negative instances is usually larger than positive
instances, thus making selecting the most informative negative instance
critical for performance. Such a selection strategy for negative instances from
each bag is an open problem that has not been well studied for weak-label
learning. In this paper, we study several sampling strategies that can measure
the usefulness of negative instances for weak-label learning and select them
accordingly. We test our method on CIFAR-10 and AudioSet datasets and show that
it improves the weak-label classification performance and reduces the
computational cost compared to random sampling methods. Our work reveals that
negative instances are not all equally irrelevant, and selecting them wisely
can benefit weak-label learning.
