Image Classification on Fashion-MNIST uniform noise η=0.5 (7-fold CV)
84.36AccuracyrSDNet
Evaluation results
| Method | Links | |
|---|---|---|
| rSDNetβ=0.05, λ=−0.82026.03 | 84.36 | |
| rSDNetβ=0.1, λ=−12026.03 | 84.05 | |
| rSDNetβ=0, λ=−0.72026.03 | 83.4 | |
| GCEq=0.72026.03 | 83.39 | |
| rSDNetβ=0, λ=−0.82026.03 | 83.16 | |
| rSDNetβ=0.05, λ=−0.72026.03 | 83.04 | |
| rSDNetβ=0.05, λ=−12026.03 | 82.59 | |
| rSDNetβ=0.1, λ=−0.82026.03 | 79.24 | |
| rSDNetβ=0.1, λ=−0.72026.03 | 73.92 | |
| MAE2026.03 | 71.79 | |
| rKLD2026.03 | 68.71 | |
| rSDNetβ=0, λ=−0.52026.03 | 68.51 | |
| TCCEparameter=0.32026.03 | 68.48 | |
| GCEq=0.52026.03 | 67.56 | |
| rSDNetβ=0.5, λ=−12026.03 | 65.35 | |
| rSDNetβ=0.05, λ=−0.52026.03 | 65.2 | |
| rSDNetβ=0.7, λ=−0.82026.03 | 65.09 | |
| rSDNetβ=0.3, λ=−0.72026.03 | 64.66 | |
| rSDNetβ=0.7, λ=−0.52026.03 | 64.65 | |
| rSDNetβ=0.5, λ=−0.52026.03 | 64.38 | |
| rSDNetβ=0.3, λ=−0.82026.03 | 63.94 | |
| rSDNetβ=0.7, λ=−0.72026.03 | 63.85 | |
| rSDNetβ=0.5, λ=−0.82026.03 | 63.81 | |
| rSDNetβ=0.5, λ=02026.03 | 63.77 | |
| rSDNetβ=0.7, λ=−12026.03 | 63.72 | |
| rSDNetβ=0.5, λ=−0.72026.03 | 63.62 | |
| TCCEparameter=0.22026.03 | 63.27 | |
| rSDNetβ=0.3, λ=−12026.03 | 63.01 | |
| rSDNetβ=1, λ=02026.03 | 62.72 | |
| SCEα=0.5, β=12026.03 | 62.35 | |
| CCE2026.03 | 62.05 | |
| rSDNetβ=0.1, λ=−0.52026.03 | 62.05 | |
| rSDNetβ=0.3, λ=−0.52026.03 | 62.05 | |
| rSDNetβ=0.7, λ=02026.03 | 61.97 | |
| FCLμ=02026.03 | 61.77 | |
| rSDNetβ=0.7, λ=0.52026.03 | 61.7 | |
| rSDNetβ=0.3, λ=02026.03 | 61.62 | |
| FCLμ=0.502026.03 | 61.23 | |
| TCCEparameter=0.12026.03 | 60.98 | |
| rSDNetβ=0.1, λ=02026.03 | 60.93 | |
| FCLμ=0.252026.03 | 60.84 | |
| rSDNetβ=0.5, λ=0.52026.03 | 60.42 | |
| FCLμ=0.752026.03 | 9.94 |