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Models/Beta-Rank

Beta-Rank

Reported on 8 benchmarks across 4 tasks · 1 paper · 4 SOTA

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

Methodology3 results

ClassificationonISIC 2017
Accuracy· 2023-04-15
72.44
SOTA
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461
ClassificationonIDRiD
Accuracy· 2023-04-15
81.88
SOTA
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461
Network PruningonCIFAR-100
Accuracy· 2023-04-15
74.01
best: 79 (Dense)
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461

Computer Vision3 results

Image ClassificationonCIFAR-10
Percentage correct· 2023-04-15
93.97
best: 99.5 (ViT-H/14)
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461
Image ClassificationonCIFAR-100
Accuracy· 2023-04-15
74.01
best: 96.808 (efficient adaptive ensembling)
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461
Image ClassificationonCIFAR-100
Percentage correct· 2023-04-15
74.01
best: 96.08 (EffNet-L2 (SAM))
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461

Medical2 results

Medical Image ClassificationonISIC 2017
Accuracy· 2023-04-15
72.44
SOTA
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461
Medical Image ClassificationonIDRiD
Accuracy· 2023-04-15
81.88
SOTA
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis arXiv:2304.07461