  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.
