Classification on MSTAR 8-shot
72.5PrecisionCr-GAN
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| Cr-GANTrain Iter.=15k2026.01 | 72.5 | 71.21 | 70.01 | 71.23 | |
| SiT(B/2)Train Iter.=15k2026.01 | 68.15 | 71.1 | 67.5 | 67.82 | |
| StyleGAN2 + ADATrain Iter.=15k2026.01 | 68.13 | 69.06 | 67.08 | 67.41 | |
| DiT(B/2)Train Iter.=15k2026.01 | 67.75 | 68.31 | 67.17 | 66.81 | |
| EDM2(With Pretrained)Train Iter.=15k2026.01 | 66.86 | 68.9 | 65.9 | 65.83 | |
| StyleGAN2 + DA + RLCTrain Iter.=15k2026.01 | 66.22 | 67.53 | 66.22 | 65.87 | |
| EDM2Train Iter.=15k2026.01 | 65.91 | 70.4 | 65.32 | 64.86 | |
| DDPMTrain Iter.=15k2026.01 | 65.2 | 70.55 | 65.1 | 65.15 | |
| DCGANTrain Iter.=15k2026.01 | 64.92 | 68.5 | 64.58 | 63.68 | |
| StyleGAN2Train Iter.=15k2026.01 | 62.98 | 65.16 | 61.65 | 61.77 | |
| R3GANTrain Iter.=15k2026.01 | 62.47 | 62.47 | 62.31 | 61.79 | |
| StyleGAN2 + DiffAugmentTrain Iter.=15k2026.01 | 60.91 | 61.75 | 59.68 | 59.49 | |
| Resnet50Train Iter.=-2026.01 | 60.34 | 60.72 | 59.8 | 58.91 | |
| Resnet18Train Iter.=-2026.01 | 59.97 | 58.26 | 59.77 | 57.33 | |
| Densenet121Train Iter.=-2026.01 | 59.33 | 60.57 | 59.05 | 57.16 | |
| resnext50Train Iter.=-2026.01 | 56.79 | 54.45 | 52.87 | 52.05 | |
| Resnext101Train Iter.=-2026.01 | 56.76 | 51.36 | 51.19 | 49.15 | |
| RegnetTrain Iter.=-2026.01 | 51.78 | 52.67 | 49.9 | 48.45 | |
| SiT(S/2)Train Iter.=15k2026.01 | 51.52 | 50.98 | 48.55 | 49.11 | |
| ViT-BaseTrain Iter.=-2026.01 | 51.27 | 47.3 | 51.89 | 48.42 | |
| EfficientnetTrain Iter.=-2026.01 | 51.15 | 50.39 | 49.26 | 48.04 | |
| Swin v2Train Iter.=-2026.01 | 49.18 | 46.71 | 44.87 | 45.59 | |
| ConvnextTrain Iter.=-2026.01 | 34.21 | 38.65 | 33.28 | 29.77 | |
| SwinTrain Iter.=-2026.01 | 30.03 | 33.72 | 34.87 | 27.15 | |
| ViT-Base(finetune-head)Train Iter.=-2026.01 | 22.55 | 23.17 | 26 | 21.56 |