ASE-NAS+

Reported on 4 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology4 results

Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
Accuracy (Val)· 2022-10-31
46.66
best: 46.73 (AG-Net)
Automated Dominative Subspace Mining for Efficient Neural Architecture Search arXiv:2210.17180
Neural Architecture SearchonNAS-Bench-201, CIFAR-100
Accuracy (Val)· 2022-10-31
73.12
best: 73.49 (DiNAS)
Automated Dominative Subspace Mining for Efficient Neural Architecture Search arXiv:2210.17180
AutoMLonNAS-Bench-201, ImageNet-16-120
Accuracy (Val)· 2022-10-31
46.66
best: 46.73 (AG-Net)
Automated Dominative Subspace Mining for Efficient Neural Architecture Search arXiv:2210.17180
AutoMLonNAS-Bench-201, CIFAR-100
Accuracy (Val)· 2022-10-31
73.12
best: 73.49 (DiNAS)
Automated Dominative Subspace Mining for Efficient Neural Architecture Search arXiv:2210.17180