Research map / Label-efficient and robust learning / Recognition and long-tail
Disentangling Label Distribution for Long-Tailed Visual Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with LADE instead of the strongest listed baseline | CIFAR-100-LT · CIFAR-100-LT (imbalance ratio 50) | top-1 accuracy: 50.3 -> 50.5 | Helped |
| Train with LADE instead of the strongest listed baseline | Places-LT · Places-LT (All) | top-1 accuracy: 37.9 -> 38.8 | Helped |
| Train with LADE instead of the strongest listed baseline | ImageNet-LT · ImageNet-LT (180 epochs, All) | top-1 accuracy: 52.8 -> 53.0 | Helped |
| Train with LADE instead of the strongest listed baseline | iNaturalist 2018 · iNaturalist 2018 (All) | top-1 accuracy: 69.8 -> 70.0 | Helped |
| Add the DV representation regularizer (ablation) | LADE · CIFAR-100-LT (imbalance ratio 10) | top-1 accuracy: 61.5 -> 61.7 | Helped |
| Add LADER (ablation) | LADE · CIFAR-100-LT (imbalance ratio 10) | top-1 accuracy: 61.6 -> 61.7 | Helped |
| Add the DV representation regularizer (ablation) | LADE · CIFAR-100-LT (imbalance ratio 50) | top-1 accuracy: 49.5 -> 50.5 | Helped |
| Add LADER (ablation) | LADE · CIFAR-100-LT (imbalance ratio 50) | top-1 accuracy: 49.9 -> 50.5 | Helped |
| Add the DV representation regularizer (ablation) | LADE · CIFAR-100-LT (imbalance ratio 100) | top-1 accuracy: 45.2 -> 45.4 | Helped |
| Add LADER (ablation) | LADE · CIFAR-100-LT (imbalance ratio 100) | top-1 accuracy: 45.1 -> 45.4 | Helped |
| Add the DV representation regularizer (ablation) | LADE · Places-LT (All) | top-1 accuracy: 38.5 -> 38.8 | Helped |
| Add LADER (ablation) | LADE · Places-LT (All) | top-1 accuracy: 38.6 -> 38.8 | Helped |
Closest papers by meaning
- Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition (ICML 2024 · similarity 0.89)
- Long-tailed Recognition with Model Rebalancing (NeurIPS 2025 · similarity 0.88)
- Feature Fusion from Head to Tail for Long-Tailed Visual Recognition (AAAI 2024 · similarity 0.88)
- Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition (ICML 2024 · similarity 0.88)
- Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition (NeurIPS 2024 · similarity 0.87)
- DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge (NeurIPS 2024 · similarity 0.87)
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