Image Classification on Fashion-MNIST uniform noise η=0.3 (7-fold CV)
87.45AccuracyrSDNet
Evaluation results
| Method | Links | |
|---|---|---|
| rSDNetβ=0.1, λ=−0.72026.03 | 87.45 | |
| rSDNetβ=0.05, λ=−0.72026.03 | 86.82 | |
| rSDNetβ=0.1, λ=−0.82026.03 | 86.37 | |
| GCEq=0.52026.03 | 85.57 | |
| rSDNetβ=0.1, λ=−12026.03 | 85.33 | |
| GCEq=0.72026.03 | 85.31 | |
| TCCEparameter=0.32026.03 | 85.09 | |
| rSDNetβ=0, λ=−0.52026.03 | 85.08 | |
| rSDNetβ=0.05, λ=−12026.03 | 84.78 | |
| rSDNetβ=0, λ=−0.82026.03 | 84.53 | |
| rSDNetβ=0.05, λ=−0.82026.03 | 84.27 | |
| rSDNetβ=0.05, λ=−0.52026.03 | 83.59 | |
| rSDNetβ=0, λ=−0.72026.03 | 83.53 | |
| rSDNetβ=0.1, λ=−0.52026.03 | 81.81 | |
| TCCEparameter=0.22026.03 | 79.23 | |
| rSDNetβ=0.3, λ=−12026.03 | 77.83 | |
| rSDNetβ=0.3, λ=−0.82026.03 | 77.19 | |
| rKLD2026.03 | 77.11 | |
| rSDNetβ=0.3, λ=−0.72026.03 | 76.43 | |
| rSDNetβ=0.3, λ=−0.52026.03 | 74.69 | |
| TCCEparameter=0.12026.03 | 73.87 | |
| rSDNetβ=0.7, λ=−0.52026.03 | 73.46 | |
| rSDNetβ=0.5, λ=−0.72026.03 | 73.35 | |
| rSDNetβ=0.5, λ=−0.82026.03 | 73.27 | |
| rSDNetβ=0.5, λ=−12026.03 | 73.16 | |
| rSDNetβ=0.7, λ=−12026.03 | 73.09 | |
| rSDNetβ=0.5, λ=−0.52026.03 | 72.86 | |
| rSDNetβ=0.7, λ=−0.72026.03 | 72.69 | |
| rSDNetβ=0.7, λ=02026.03 | 72.47 | |
| rSDNetβ=1, λ=02026.03 | 72.4 | |
| rSDNetβ=0.7, λ=−0.82026.03 | 72.37 | |
| MAE2026.03 | 72.24 | |
| FCLμ=0.502026.03 | 72.02 | |
| SCEα=0.5, β=12026.03 | 71.89 | |
| rSDNetβ=0.7, λ=0.52026.03 | 71.67 | |
| rSDNetβ=0.5, λ=02026.03 | 70.83 | |
| FCLμ=02026.03 | 70.28 | |
| FCLμ=0.252026.03 | 69.9 | |
| rSDNetβ=0.5, λ=0.52026.03 | 69.76 | |
| rSDNetβ=0.3, λ=02026.03 | 69.75 | |
| CCE2026.03 | 69.55 | |
| rSDNetβ=0.1, λ=02026.03 | 69.22 | |
| FCLμ=0.752026.03 | 9.89 |