  In this paper, we tackle the long-tailed visual recognition problem from the
knowledge distillation perspective by proposing a Distill the Virtual Examples
(DiVE) method. Specifically, by treating the predictions of a teacher model as
virtual examples, we prove that distilling from these virtual examples is
equivalent to label distribution learning under certain constraints. We show
that when the virtual example distribution becomes flatter than the original
input distribution, the under-represented tail classes will receive significant
improvements, which is crucial in long-tailed recognition. The proposed DiVE
method can explicitly tune the virtual example distribution to become flat.
Extensive experiments on three benchmark datasets, including the large-scale
iNaturalist ones, justify that the proposed DiVE method can significantly
outperform state-of-the-art methods. Furthermore, additional analyses and
experiments verify the virtual example interpretation, and demonstrate the
effectiveness of tailored designs in DiVE for long-tailed problems.
