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Models/ResNet-34

ResNet-34

Reported on 31 benchmarks across 3 tasks · 2 papers · 8 SOTA

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

Computer Vision25 results

Out-of-Distribution DetectiononCIFAR-10 vs LSUN (R)
AUROC· 2021-10-18
100
SOTA
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs LSUN (C)
AUROC· 2021-10-18
97.8
SOTA
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs Gaussian
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs Uniform
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs LSUN (C)
AUROC· 2021-10-18
99.5
best: 99.9 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs iSUN
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs ImageNet (C)
AUROC· 2021-10-18
98.4
best: 99 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs iSUN
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs SVHN
AUROC· 2021-10-18
97.9
best: 98.7 (OECC + MD)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs Gaussian
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs Uniform
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs ImageNet (R)
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs ImageNet (C)
AUROC· 2021-10-18
99.8
best: 99.9 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs LSUN (R)
AUROC· 2021-10-18
99.6
best: 99.7 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs Gaussian
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs CIFAR-100
AUROC· 2021-10-18
99.8
best: 100 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs CIFAR-10
AUROC· 2021-10-18
99.8
best: 100 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs SVHN
AUROC· 2021-10-18
99.8
best: 100 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs Uniform
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-10 vs ImageNet (R)
AUROC· 2021-10-18
99.9
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs ImageNet (C)
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs LSUN (R)
AUROC· 2021-10-18
100
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs iSUN
AUROC· 2021-10-18
99.3
best: 99.5 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononCIFAR-100 vs ImageNet (R)
AUROC· 2021-10-18
99.2
best: 99.5 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246
Out-of-Distribution DetectiononSVHN vs LSUN (C)
AUROC· 2021-10-18
99.9
best: 100 (DenseNet-BC-100)
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection arXiv:2110.09246

Audio3 results

Audio ClassificationonICBHI Respiratory Sound Database
ICBHI Score· uses extra data· 2020-10-31
56.2
best: 65.53 (ADD)
SOTA
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting arXiv:2011.00196
Audio ClassificationonICBHI Respiratory Sound Database
Sensitivity· uses extra data· 2020-10-31
40.1
best: 48.21 (BEATs (CE))
SOTA
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting arXiv:2011.00196
Audio ClassificationonICBHI Respiratory Sound Database
Specificity· uses extra data· 2020-10-31
72.3
best: 85.13 (ADD)
SOTA
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting arXiv:2011.00196

Methodology3 results

ClassificationonICBHI Respiratory Sound Database
ICBHI Score· uses extra data· 2020-10-31
56.2
best: 65.53 (ADD)
SOTA
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting arXiv:2011.00196
ClassificationonICBHI Respiratory Sound Database
Sensitivity· uses extra data· 2020-10-31
40.1
best: 48.21 (BEATs (CE))
SOTA
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting arXiv:2011.00196
ClassificationonICBHI Respiratory Sound Database
Specificity· uses extra data· 2020-10-31
72.3
best: 85.13 (ADD)
SOTA
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting arXiv:2011.00196