Image Classification on Fashion-MNIST uniform noise η=0.1 (7-fold CV)
88.92AccuracyrSDNet
Evaluation results
| Method | Links | |
|---|---|---|
| rSDNetβ=0.05, λ=−0.52026.03 | 88.92 | |
| rSDNetβ=0, λ=−0.52026.03 | 88.88 | |
| rSDNetβ=0.3, λ=−0.82026.03 | 88.84 | |
| rSDNetβ=0.3, λ=−0.72026.03 | 88.83 | |
| rSDNetβ=0.1, λ=−0.52026.03 | 88.73 | |
| GCEq=0.52026.03 | 88.68 | |
| rSDNetβ=0.3, λ=−12026.03 | 88.65 | |
| rSDNetβ=0.3, λ=−0.52026.03 | 88.46 | |
| rSDNetβ=0.5, λ=−0.52026.03 | 88.25 | |
| rSDNetβ=0.5, λ=−0.82026.03 | 87.98 | |
| rSDNetβ=0.1, λ=−0.72026.03 | 87.85 | |
| rSDNetβ=0.5, λ=−0.72026.03 | 87.72 | |
| TCCEparameter=0.12026.03 | 87.71 | |
| TCCEparameter=0.22026.03 | 87.69 | |
| rSDNetβ=0.5, λ=−12026.03 | 87.57 | |
| rSDNetβ=0.7, λ=−0.52026.03 | 87.31 | |
| rSDNetβ=0.7, λ=−0.72026.03 | 87.21 | |
| rSDNetβ=0.05, λ=−0.72026.03 | 87.06 | |
| rSDNetβ=0, λ=−0.72026.03 | 86.94 | |
| rSDNetβ=0.7, λ=02026.03 | 86.91 | |
| GCEq=0.72026.03 | 86.88 | |
| rSDNetβ=0.7, λ=−0.82026.03 | 86.87 | |
| rSDNetβ=0.1, λ=−0.82026.03 | 86.84 | |
| rSDNetβ=0.7, λ=−12026.03 | 86.78 | |
| rSDNetβ=1, λ=02026.03 | 86.49 | |
| rSDNetβ=0, λ=−0.82026.03 | 86.15 | |
| rSDNetβ=0.05, λ=−0.82026.03 | 86.12 | |
| rSDNetβ=0.05, λ=−12026.03 | 85.81 | |
| rSDNetβ=0.1, λ=−12026.03 | 85.65 | |
| rSDNetβ=0.5, λ=02026.03 | 85.42 | |
| rSDNetβ=0.7, λ=0.52026.03 | 85.35 | |
| rKLD2026.03 | 84.59 | |
| rSDNetβ=0.3, λ=02026.03 | 82.83 | |
| SCEα=0.5, β=12026.03 | 82.41 | |
| FCLμ=0.252026.03 | 81.89 | |
| rSDNetβ=0.5, λ=0.52026.03 | 81.38 | |
| FCLμ=02026.03 | 81.25 | |
| FCLμ=0.502026.03 | 80.51 | |
| rSDNetβ=0.1, λ=02026.03 | 80.37 | |
| CCE2026.03 | 80.15 | |
| MAE2026.03 | 79.1 | |
| TCCEparameter=0.32026.03 | 61.18 | |
| FCLμ=0.752026.03 | 9.78 |