Distilling Virtual Examples for Long-tailed Recognition

TL;DR

This paper introduces DiVE, a knowledge distillation method that uses virtual examples to improve long-tailed visual recognition by flattening the virtual example distribution, leading to better tail class performance.

Contribution

The paper proposes a novel distillation approach that treats teacher predictions as virtual examples, explicitly tuning their distribution to enhance long-tailed recognition.

Findings

DiVE significantly outperforms state-of-the-art methods on benchmark datasets.

Flattening the virtual example distribution benefits tail class recognition.

The virtual example interpretation is validated through extensive analysis.

Abstract

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.