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Models/OPeN (WideResNet-28-10)

OPeN (WideResNet-28-10)

Reported on 25 benchmarks across 5 tasks · 1 paper · 20 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 ClassificationonCelebA-5
Error Rate· 2021-12-16
19.1
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=50)
Error Rate· 2021-12-16
40.2
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot ClassificationonCIFAR-10-LT (ρ=50)
Error Rate· 2021-12-16
10.8
best: 8.44 (GLMC + SAM)
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot ClassificationonCIFAR-10-LT (ρ=100)
Error Rate· 2021-12-16
13.9
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Long-tail LearningonCelebA-5
Error Rate· 2021-12-16
19.1
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Long-tail LearningonCIFAR-100-LT (ρ=50)
Error Rate· 2021-12-16
40.2
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Long-tail LearningonCIFAR-10-LT (ρ=50)
Error Rate· 2021-12-16
10.8
best: 8.44 (GLMC + SAM)
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Long-tail LearningonCIFAR-10-LT (ρ=100)
Error Rate· 2021-12-16
13.9
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot LearningonCelebA-5
Error Rate· 2021-12-16
19.1
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=50)
Error Rate· 2021-12-16
40.2
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot LearningonCIFAR-10-LT (ρ=50)
Error Rate· 2021-12-16
10.8
best: 8.44 (GLMC + SAM)
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot LearningonCIFAR-10-LT (ρ=100)
Error Rate· 2021-12-16
13.9
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2021-12-16
45.8
best: 10.9 (LPT)
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Long-tail LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2021-12-16
45.8
best: 10.9 (LPT)
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2021-12-16
45.8
best: 10.9 (LPT)
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810

Computer Vision10 results

Image ClassificationonCelebA-5
Error Rate· 2021-12-16
19.1
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Image ClassificationonCIFAR-100-LT (ρ=50)
Error Rate· 2021-12-16
40.2
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Image ClassificationonCIFAR-10-LT (ρ=50)
Error Rate· 2021-12-16
10.8
best: 8.44 (GLMC + SAM)
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Image ClassificationonCIFAR-10-LT (ρ=100)
Error Rate· 2021-12-16
13.9
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Few-Shot Image ClassificationonCelebA-5
Error Rate· 2021-12-16
19.1
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=50)
Error Rate· 2021-12-16
40.2
best: 9.8 (LIFT (ViT-B/16, ImageNet-21K pre-training))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Few-Shot Image ClassificationonCIFAR-10-LT (ρ=50)
Error Rate· 2021-12-16
10.8
best: 8.44 (GLMC + SAM)
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Few-Shot Image ClassificationonCIFAR-10-LT (ρ=100)
Error Rate· 2021-12-16
13.9
best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
SOTA
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2021-12-16
45.8
best: 10.9 (LPT)
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2021-12-16
45.8
best: 10.9 (LPT)
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images arXiv:2112.08810