  Negative sampling schemes enable efficient training given a large number of
classes, by offering a means to approximate a computationally expensive loss
function that takes all labels into account. In this paper, we present a new
connection between these schemes and loss modification techniques for
countering label imbalance. We show that different negative sampling schemes
implicitly trade-off performance on dominant versus rare labels. Further, we
provide a unified means to explicitly tackle both sampling bias, arising from
working with a subset of all labels, and labeling bias, which is inherent to
the data due to label imbalance. We empirically verify our findings on
long-tail classification and retrieval benchmarks.
