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Models/LDAM

LDAM

Reported on 45 benchmarks across 5 tasks · 3 papers · 30 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology27 results

Generalized Few-Shot ClassificationonCUB-LT
Long-Tailed Accuracy· 2019-06-18
64.1
best: 67.7 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot ClassificationonCUB-LT
Per-Class Accuracy· 2019-06-18
50.1
best: 60.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot ClassificationonAWA-LT
Long-Tailed Accuracy· 2019-06-18
93.5
best: 94.1 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot ClassificationonAWA-LT
Per-Class Accuracy· 2019-06-18
69.1
best: 76.2 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot ClassificationonSUN-LT
Long-Tailed Accuracy· 2019-06-18
36.4
best: 40.4 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot ClassificationonSUN-LT
Per-Class Accuracy· 2019-06-18
29.8
best: 36.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Long-tail LearningonCUB-LT
Long-Tailed Accuracy· 2019-06-18
64.1
best: 67.7 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Long-tail LearningonCUB-LT
Per-Class Accuracy· 2019-06-18
50.1
best: 60.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Long-tail LearningonAWA-LT
Long-Tailed Accuracy· 2019-06-18
93.5
best: 94.1 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Long-tail LearningonAWA-LT
Per-Class Accuracy· 2019-06-18
69.1
best: 76.2 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Long-tail LearningonSUN-LT
Long-Tailed Accuracy· 2019-06-18
36.4
best: 40.4 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Long-tail LearningonSUN-LT
Per-Class Accuracy· 2019-06-18
29.8
best: 36.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot LearningonCUB-LT
Long-Tailed Accuracy· 2019-06-18
64.1
best: 67.7 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot LearningonCUB-LT
Per-Class Accuracy· 2019-06-18
50.1
best: 60.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot LearningonAWA-LT
Long-Tailed Accuracy· 2019-06-18
93.5
best: 94.1 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot LearningonAWA-LT
Per-Class Accuracy· 2019-06-18
69.1
best: 76.2 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot LearningonSUN-LT
Long-Tailed Accuracy· 2019-06-18
36.4
best: 40.4 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot LearningonSUN-LT
Per-Class Accuracy· 2019-06-18
29.8
best: 36.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Generalized Few-Shot ClassificationonNIH-CXR-LT
Balanced Accuracy· 2022-08-29
0.178
best: 0.294 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Generalized Few-Shot ClassificationonMIMIC-CXR-LT
Balanced Accuracy· 2022-08-29
0.165
best: 0.296 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Long-tail LearningonNIH-CXR-LT
Balanced Accuracy· 2022-08-29
0.178
best: 0.294 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Long-tail LearningonMIMIC-CXR-LT
Balanced Accuracy· 2022-08-29
0.165
best: 0.296 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Generalized Few-Shot LearningonNIH-CXR-LT
Balanced Accuracy· 2022-08-29
0.178
best: 0.294 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Generalized Few-Shot LearningonMIMIC-CXR-LT
Balanced Accuracy· 2022-08-29
0.165
best: 0.296 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Generalized Few-Shot ClassificationonImageNet-GLT
Accuracy· 2022-07-19
38.54
best: 45.64 (RIDE + IFL)
Invariant Feature Learning for Generalized Long-Tailed Classification arXiv:2207.09504
Long-tail LearningonImageNet-GLT
Accuracy· 2022-07-19
38.54
best: 45.64 (RIDE + IFL)
Invariant Feature Learning for Generalized Long-Tailed Classification arXiv:2207.09504
Generalized Few-Shot LearningonImageNet-GLT
Accuracy· 2022-07-19
38.54
best: 45.64 (RIDE + IFL)
Invariant Feature Learning for Generalized Long-Tailed Classification arXiv:2207.09504

Computer Vision18 results

Image ClassificationonCUB-LT
Long-Tailed Accuracy· 2019-06-18
64.1
best: 67.7 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Image ClassificationonCUB-LT
Per-Class Accuracy· 2019-06-18
50.1
best: 60.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Image ClassificationonAWA-LT
Long-Tailed Accuracy· 2019-06-18
93.5
best: 94.1 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Image ClassificationonAWA-LT
Per-Class Accuracy· 2019-06-18
69.1
best: 76.2 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Image ClassificationonSUN-LT
Long-Tailed Accuracy· 2019-06-18
36.4
best: 40.4 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Image ClassificationonSUN-LT
Per-Class Accuracy· 2019-06-18
29.8
best: 36.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Few-Shot Image ClassificationonCUB-LT
Long-Tailed Accuracy· 2019-06-18
64.1
best: 67.7 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Few-Shot Image ClassificationonCUB-LT
Per-Class Accuracy· 2019-06-18
50.1
best: 60.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Few-Shot Image ClassificationonAWA-LT
Long-Tailed Accuracy· 2019-06-18
93.5
best: 94.1 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Few-Shot Image ClassificationonAWA-LT
Per-Class Accuracy· 2019-06-18
69.1
best: 76.2 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Few-Shot Image ClassificationonSUN-LT
Long-Tailed Accuracy· 2019-06-18
36.4
best: 40.4 (DRAGON)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Few-Shot Image ClassificationonSUN-LT
Per-Class Accuracy· 2019-06-18
29.8
best: 36.1 (DRAGON + Bal'Loss)
SOTA
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss arXiv:1906.07413
Image ClassificationonNIH-CXR-LT
Balanced Accuracy· 2022-08-29
0.178
best: 0.294 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Image ClassificationonMIMIC-CXR-LT
Balanced Accuracy· 2022-08-29
0.165
best: 0.296 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Few-Shot Image ClassificationonNIH-CXR-LT
Balanced Accuracy· 2022-08-29
0.178
best: 0.294 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Few-Shot Image ClassificationonMIMIC-CXR-LT
Balanced Accuracy· 2022-08-29
0.165
best: 0.296 (Decoupling (cRT))
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study arXiv:2208.13365
Image ClassificationonImageNet-GLT
Accuracy· 2022-07-19
38.54
best: 45.64 (RIDE + IFL)
Invariant Feature Learning for Generalized Long-Tailed Classification arXiv:2207.09504
Few-Shot Image ClassificationonImageNet-GLT
Accuracy· 2022-07-19
38.54
best: 45.64 (RIDE + IFL)
Invariant Feature Learning for Generalized Long-Tailed Classification arXiv:2207.09504