DCNN+GFE

Reported on 3 benchmarks across 1 task · 1 paper

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

Computer Vision3 results

Image ClassificationonCIFAR-10
Percentage correct· 2017-10-06
89.1
best: 99.5 (ViT-H/14)
Deep Convolutional Neural Networks as Generic Feature Extractors arXiv:1710.02286
Image ClassificationonCIFAR-100
Percentage correct· 2017-10-06
67.7
best: 96.08 (EffNet-L2 (SAM))
Deep Convolutional Neural Networks as Generic Feature Extractors arXiv:1710.02286
Image ClassificationonMNIST
Percentage error· 2017-10-06
0.5
best: 0.13 (Branching/Merging CNN + Homogeneous Vector Capsules)
Deep Convolutional Neural Networks as Generic Feature Extractors arXiv:1710.02286