Image Classification On Kuzushiji Mnist
Metriken
Accuracy
Error
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy | Error |
|---|---|---|
| training-neural-networks-with-local-error | 99.01 | 0.99 |
| cnn-filter-db-an-empirical-investigation-of | 98.75 | - |
| toward-understanding-supervised | 98.63 | - |
| toward-understanding-supervised | 98.61 | - |
| efficient-global-neural-architecture-search | 99.35 | - |
| multi-complementary-and-unlabeled-learning | 79.90 | - |
| the-convolutional-tsetlin-machine | 96.3 | - |
| a-comprehensive-study-of-imagenet-pre | 98.79 | - |
| efficient-global-neural-architecture-search | 99.29 | - |
| mixup-beyond-empirical-risk-minimization | 98.41 | - |
| multi-complementary-and-unlabeled-learning | 79.5 | - |
| deep-learning-for-classical-japanese | - | 1.10 |
| toward-understanding-supervised | 98.60 | - |
| improved-efficient-capsule-network-for | 98.43 | - |
| complementary-label-learning-for-arbitrary | 67.1 | - |
| toward-understanding-supervised | 98.68 | - |
| kercnns-biologically-inspired-lateral | 93.13 | - |
| context-aware-multipath-networks | 99.05 | 0.95 |
| toward-understanding-supervised | 98.80 | - |
| toward-understanding-supervised | 98.81 | - |
| spinalnet-deep-neural-network-with-gradual-1 | 99.15 | 0.85 |
| identity-mappings-in-deep-residual-networks | 97.82 | - |
| learning-local-discrete-features-in | 98.78 | 1.22 |
| toward-understanding-supervised | 98.72 | - |
| toward-understanding-supervised | 98.66 | - |
| toward-understanding-supervised | 98.84 | - |