Image Classification on Fashion-MNIST uniform noise η=0.2 (7-fold CV)
88.32AccuracyrSDNet
Evaluation Results
| Method | Links | |
|---|---|---|
| rSDNetβ=0, λ=−0.52026.03 | 88.32 | |
| GCEq=0.52026.03 | 88.16 | |
| rSDNetβ=0.05, λ=−0.52026.03 | 87.88 | |
| rSDNetβ=0.1, λ=−0.72026.03 | 87.58 | |
| rSDNetβ=0.1, λ=−0.52026.03 | 87.49 | |
| rSDNetβ=0.05, λ=−0.72026.03 | 87.17 | |
| TCCEparameter=0.32026.03 | 87.15 | |
| GCEq=0.72026.03 | 86.58 | |
| rSDNetβ=0.3, λ=−12026.03 | 86.51 | |
| rSDNetβ=0.1, λ=−0.82026.03 | 86.49 | |
| rSDNetβ=0.3, λ=−0.82026.03 | 86.48 | |
| rSDNetβ=0, λ=−0.72026.03 | 86.4 | |
| rSDNetβ=0.3, λ=−0.72026.03 | 86.31 | |
| TCCEparameter=0.22026.03 | 86.3 | |
| rSDNetβ=0.05, λ=−0.82026.03 | 86.17 | |
| rSDNetβ=0, λ=−0.82026.03 | 84.94 | |
| rSDNetβ=0.1, λ=−12026.03 | 84.8 | |
| rSDNetβ=0.3, λ=−0.52026.03 | 84.4 | |
| rSDNetβ=0.05, λ=−12026.03 | 83.81 | |
| rKLD2026.03 | 80.99 | |
| rSDNetβ=0.5, λ=−0.52026.03 | 80.85 | |
| TCCEparameter=0.12026.03 | 80.7 | |
| rSDNetβ=0.5, λ=−0.72026.03 | 80.61 | |
| rSDNetβ=0.5, λ=−0.82026.03 | 80.44 | |
| rSDNetβ=1, λ=02026.03 | 79.79 | |
| rSDNetβ=0.7, λ=−0.52026.03 | 79.31 | |
| rSDNetβ=0.5, λ=−12026.03 | 79.28 | |
| rSDNetβ=0.7, λ=−0.82026.03 | 79.2 | |
| MAE2026.03 | 79.07 | |
| rSDNetβ=0.7, λ=−12026.03 | 78.9 | |
| rSDNetβ=0.7, λ=−0.72026.03 | 78.77 | |
| rSDNetβ=0.7, λ=02026.03 | 78.48 | |
| rSDNetβ=0.5, λ=02026.03 | 77.85 | |
| rSDNetβ=0.7, λ=0.52026.03 | 77.85 | |
| SCEα=0.5, β=12026.03 | 77.81 | |
| rSDNetβ=0.3, λ=02026.03 | 75.41 | |
| FCLμ=0.252026.03 | 75.14 | |
| FCLμ=02026.03 | 75.13 | |
| rSDNetβ=0.1, λ=02026.03 | 74.35 | |
| rSDNetβ=0.5, λ=0.52026.03 | 74.25 | |
| CCE2026.03 | 74.01 | |
| FCLμ=0.502026.03 | 68.45 | |
| FCLμ=0.752026.03 | 9.9 |