Image Classification on USPS
99.5AccuracyThermoLion
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| ThermoLion2025.12 | 99.5 | — | — | |
| BioTuneBackbone=ResNet-502026.01 | 97.57 | 0.5 | — | |
| L1-SPBackbone=ResNet-502026.01 | 97.31 | — | — | |
| FTBackbone=ResNet-502026.01 | 97.05 | — | — | |
| L2-SPBackbone=ResNet-502026.01 | 97 | — | — | |
| LoRABackbone=ResNet-502026.01 | 96.92 | — | — | |
| AutoRGNBackbone=ResNet-502026.01 | 96.91 | — | — | |
| G-FLBackbone=ResNet-502026.01 | 96.86 | — | — | |
| G-LFBackbone=ResNet-502026.01 | 96.72 | — | — | |
| Adam2025.12 | 96.64 | — | — | |
| MuAdam2025.12 | 95.78 | — | — | |
| TeacherBackbone=LeNet52026.04 | 95.47 | — | — | |
| DIP-KDBackbone=LeNet5, Synthetic Dataset Size=50K2026.04 | 94.2 | — | — | |
| DFHL-RSBackbone=LeNet5, Synthetic Dataset Size=50K2026.04 | 93.97 | — | — | |
| IDEALBackbone=LeNet5, Synthetic Dataset Size=50K2026.04 | 92.66 | — | — | |
| LPBackbone=ResNet-502026.01 | 91.78 | — | — | |
| ZSDB3Backbone=LeNet5, Synthetic Dataset Size=50K2026.04 | 65.5 | — | — | |
| NaiveKDBackbone=LeNet5, Synthetic Dataset Size=50K2026.04 | 42.05 | — | — | |
| CirculantNumber of random features (k)=2562016.05 | — | — | 7.54 | |
| CirculantNumber of random features (k)=12802016.05 | — | — | 4.53 | |
| FastfoodNumber of random features (k)=2562016.05 | — | — | 7.37 | |
| FastfoodNumber of random features (k)=12802016.05 | — | — | 4.62 | |
| GaussianNumber of random features (k)=2562016.05 | — | — | 7.12 | |
| GaussianNumber of random features (k)=12802016.05 | — | — | 4.52 | |
| QMC (Halton)Number of random features (k)=2562016.05 | — | — | 6.9 | |
| QMC (Halton)Number of random features (k)=12802016.05 | — | — | 4.73 | |
| ToeplitzLike(1)Number of random features (k)=2562016.05 | — | — | 7.72 | |
| ToeplitzLike(1)Number of random features (k)=12802016.05 | — | — | 4.62 | |
| ToeplitzLike(10)Number of random features (k)=2562016.05 | — | — | 7.46 | |
| ToeplitzLike(10)Number of random features (k)=12802016.05 | — | — | 4.53 | |
| ToeplitzLike(20)Number of random features (k)=2562016.05 | — | — | 7.29 | |
| ToeplitzLike(20)Number of random features (k)=12802016.05 | — | — | 4.65 | |
| ToeplitzLike(5)Number of random features (k)=2562016.05 | — | — | 7.44 | |
| ToeplitzLike(5)Number of random features (k)=12802016.05 | — | — | 4.58 |