Accuracy Prediction on NAS-Bench-101 (depth=2~7, all samples)
0.891Kendall's TauNAR-Former
Evaluation Results
| Method | Links | |
|---|---|---|
| NAR-FormerBackbone=Transformer, Training Samples=5% (21180)2022.11 | 0.891 | |
| NAR-FormerBackbone=Transformer, Training Samples=1% (4236)2022.11 | 0.871 | |
| TNASPBackbone=Transformer, Training Samples=1% (4236)2022.11 | 0.82 | |
| TNASP + SEBackbone=Transformer, Training Samples=1% (4236)2022.11 | 0.82 | |
| ReNASBackbone=CNN, Training Samples=1% (4236)2022.11 | 0.816 | |
| NAO+SEBackbone=LSTM, Training Samples=1% (4236)2022.11 | 0.787 | |
| NAOBackbone=LSTM, Training Samples=1% (4236)2022.11 | 0.775 | |
| NP + SEBackbone=GCN, Training Samples=1% (4236)2022.11 | 0.773 | |
| NPBackbone=GCN, Training Samples=1% (4236)2022.11 | 0.769 | |
| NAR-FormerBackbone=Transformer, Training Samples=0.1% (424)2022.11 | 0.765 | |
| TNASP + SEBackbone=Transformer, Training Samples=0.1% (424)2022.11 | 0.722 | |
| TNASPBackbone=Transformer, Training Samples=0.1% (424)2022.11 | 0.705 | |
| NP + SEBackbone=GCN, Training Samples=0.1% (424)2022.11 | 0.684 | |
| NAO+SEBackbone=LSTM, Training Samples=0.1% (424)2022.11 | 0.68 | |
| NPBackbone=GCN, Training Samples=0.1% (424)2022.11 | 0.679 | |
| NAOBackbone=LSTM, Training Samples=0.1% (424)2022.11 | 0.666 | |
| ReNASBackbone=CNN, Training Samples=0.1% (424)2022.11 | 0.657 |