Papers › Distilling Virtual Examples for Long-tailed Recognition
Distilling Virtual Examples for Long-tailed Recognition
Yin-Yin He, Jianxin Wu, Xiu-Shen Wei
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-tail Learning | ImageNet-LT | RIDE-DiVE | Top-1 Accuracy | 57.12 | #31 of 69 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | DiVE | Top-1 Accuracy | 53.1 | #44 of 69 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | RIDE-DiVE | Top-1 Accuracy | 73.44% | #22 of 43 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | DiVE | Top-1 Accuracy | 71.71% | #28 of 43 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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