Classification on MSTAR (4-shot)
60.65PrecisionSiT(B/2)
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| SiT(B/2)Train Iter.=15k2026.01 | 60.65 | 60.01 | 55.64 | 56 | |
| Cr-GANTrain Iter.=15k2026.01 | 58.4 | 60.06 | 58.25 | 57.37 | |
| EDM2(With Pretrained)Train Iter.=15k2026.01 | 57.45 | 52.7 | 51.9 | 51.81 | |
| R3GANTrain Iter.=15k2026.01 | 56.5 | 57.04 | 51.05 | 52.27 | |
| DDPMTrain Iter.=15k2026.01 | 56.15 | 52.45 | 52.3 | 52.25 | |
| StyleGAN2 + ADATrain Iter.=15k2026.01 | 53.04 | 56.85 | 53.35 | 52.63 | |
| DCGANTrain Iter.=15k2026.01 | 52.8 | 51.63 | 49.76 | 48.73 | |
| StyleGAN2 + DiffAugmentTrain Iter.=15k2026.01 | 51.62 | 51.14 | 50.64 | 49.3 | |
| EDM2Train Iter.=15k2026.01 | 50.41 | 46.52 | 43.27 | 42.9 | |
| EfficientnetTrain Iter.=-2026.01 | 48.85 | 38.46 | 39.02 | 36.25 | |
| DiT(B/2)Train Iter.=15k2026.01 | 48.39 | 47.43 | 46.21 | 44.88 | |
| Densenet121Train Iter.=-2026.01 | 46.84 | 45.43 | 40.88 | 37.78 | |
| Resnext101Train Iter.=-2026.01 | 44.74 | 43.62 | 43.12 | 38.97 | |
| StyleGAN2Train Iter.=15k2026.01 | 43.45 | 47.17 | 42.58 | 41.29 | |
| Resnet18Train Iter.=-2026.01 | 42.87 | 39.99 | 41.41 | 39.29 | |
| StyleGAN2 + DA + RLCTrain Iter.=15k2026.01 | 40.48 | 42.93 | 41.12 | 38.59 | |
| SiT(S/2)Train Iter.=15k2026.01 | 40.36 | 43.15 | 39.37 | 38.47 | |
| resnext50Train Iter.=-2026.01 | 39.97 | 35.72 | 35.33 | 31.85 | |
| ViT-BaseTrain Iter.=-2026.01 | 35.85 | 29.66 | 29.59 | 28.05 | |
| RegnetTrain Iter.=-2026.01 | 32.87 | 36.99 | 31.43 | 25.46 | |
| Resnet50Train Iter.=-2026.01 | 29.03 | 36.34 | 32.04 | 27.22 | |
| SwinTrain Iter.=-2026.01 | 23.31 | 32.13 | 21.83 | 16.67 | |
| ConvnextTrain Iter.=-2026.01 | 22.88 | 29.82 | 20.19 | 13.97 | |
| ViT-Base(finetune-head)Train Iter.=-2026.01 | 22.09 | 20.89 | 20.96 | 20.89 | |
| Swin v2Train Iter.=-2026.01 | 1.83 | 18.33 | 10 | 3.1 |