Image Classification on CIFAR-10 (Mean/Std Accuracy)
99.7Top-1 AccuracyAdaptive DBN (TS)
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Adaptive DBN (TS)No. of student models=5, Evaluation protocol=10-fold cross validation2025.11 | 99.7 | — | — | — | — | — | — | 0.1 | |
| Adaptive DBNEvaluation protocol=10-fold cross validation2025.11 | 97.4 | — | — | — | — | — | — | 0.8 | |
| Convolutional NN (Fract. Max Pooling)2025.11 | 96.5 | — | — | — | — | — | — | — | |
| Convolutional NN (Wide ResNet)2025.11 | 96 | — | — | — | — | — | — | — | |
| Convolutional NN (ELU-Network)2025.11 | 93.4 | — | — | — | — | — | — | — | |
| Convolutional NN (MaxOut)2025.11 | 88.3 | — | — | — | — | — | — | — | |
| CTMdmodel=512, Backbone=HRF, Steps=600K2026.05 | — | 86.16 | — | — | — | — | — | — | |
| TIDEdmodel=512, Backbone=HRF, Steps=600K2026.05 | — | 90.6 | 90.5 | 90.57 | 4 | 90.48 | 4 | — |