Papers › Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
Saptarshi Sinha, Hiroki Ohashi
Long-tailed datasets, where head classes comprise much more training samples than tail classes, cause recognition models to get biased towards the head classes. Weighted loss is one of the most popular ways of mitigating this issue, and a recent work has suggested that class-difficulty might be a better clue than conventionally used class-frequency to decide the distribution of weights. A heuristic formulation was used in the previous work for quantifying the difficulty, but we empirically find that the optimal formulation varies depending on the characteristics of datasets. Therefore, we propose Difficulty-Net, which learns to predict the difficulty of classes using the model's performance in a meta-learning framework. To make it learn reasonable difficulty of a class within the context of other classes, we newly introduce two key concepts, namely the relative difficulty and the driver loss. The former helps Difficulty-Net take other classes into account when calculating difficulty of a class, while the latter is indispensable for guiding the learning to a meaningful direction. Extensive experiments on popular long-tailed datasets demonstrated the effectiveness of the proposed method, and it achieved state-of-the-art performance on multiple long-tailed datasets.
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 | CIFAR-100-LT (ρ=10) | Difficulty-Net | Error Rate | 34.78 | #12 of 31 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | Difficulty-Net | Error Rate | 47.04 | #22 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=50) | Difficulty-Net | Error Rate | 43.1 | #15 of 25 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | Difficulty-Net (ResNet-50 using RandAugment, single model) | Top-1 Accuracy | 57.4 | #30 of 69 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | Difficulty-Net (ResNet-50 w/o using RandAugment, single model) | Top-1 Accuracy | 54.0 | #40 of 69 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | Difficulty-Net (ResNet-10 w/o using RandAugment, single model | Top-1 Accuracy | 44.6 | #58 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | Difficulty-Net (ResNet-152) | Top-1 Accuracy | 41.7 | #12 of 29 | 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.
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