Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/LDAM-DRW + SSP

LDAM-DRW + SSP

Reported on 25 benchmarks across 5 tasks · 1 paper · 10 SOTA

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

Methodology15 results

Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=50)
Error Rate· 2020-06-13
52.89
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=10)
Error Rate· 2020-06-13
41.09
best: 8.7 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Long-tail LearningonCIFAR-100-LT (ρ=50)
Error Rate· 2020-06-13
52.89
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Long-tail LearningonCIFAR-100-LT (ρ=10)
Error Rate· 2020-06-13
41.09
best: 8.7 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=50)
Error Rate· 2020-06-13
52.89
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=10)
Error Rate· 2020-06-13
41.09
best: 8.7 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot ClassificationonCIFAR-10-LT (ρ=10)
Error Rate· 2020-06-13
11.47
best: 5 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2020-06-13
56.57
best: 10.9 (LPT)
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot ClassificationonCIFAR-10-LT (ρ=100)
Error Rate· 2020-06-13
22.17
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Long-tail LearningonCIFAR-10-LT (ρ=10)
Error Rate· 2020-06-13
11.47
best: 5 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Long-tail LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2020-06-13
56.57
best: 10.9 (LPT)
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Long-tail LearningonCIFAR-10-LT (ρ=100)
Error Rate· 2020-06-13
22.17
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot LearningonCIFAR-10-LT (ρ=10)
Error Rate· 2020-06-13
11.47
best: 5 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2020-06-13
56.57
best: 10.9 (LPT)
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Generalized Few-Shot LearningonCIFAR-10-LT (ρ=100)
Error Rate· 2020-06-13
22.17
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529

Computer Vision10 results

Image ClassificationonCIFAR-100-LT (ρ=50)
Error Rate· 2020-06-13
52.89
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Image ClassificationonCIFAR-100-LT (ρ=10)
Error Rate· 2020-06-13
41.09
best: 8.7 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=50)
Error Rate· 2020-06-13
52.89
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=10)
Error Rate· 2020-06-13
41.09
best: 8.7 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Image ClassificationonCIFAR-10-LT (ρ=10)
Error Rate· 2020-06-13
11.47
best: 5 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2020-06-13
56.57
best: 10.9 (LPT)
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Image ClassificationonCIFAR-10-LT (ρ=100)
Error Rate· 2020-06-13
22.17
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Few-Shot Image ClassificationonCIFAR-10-LT (ρ=10)
Error Rate· 2020-06-13
11.47
best: 5 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2020-06-13
56.57
best: 10.9 (LPT)
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529
Few-Shot Image ClassificationonCIFAR-10-LT (ρ=100)
Error Rate· 2020-06-13
22.17
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
Rethinking the Value of Labels for Improving Class-Imbalanced Learning arXiv:2006.07529