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Models/CDARTS

CDARTS

Reported on 10 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.

Methodology10 results

  • Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
    Accuracy (Test)· 2020-06-18
    45.51
    best: 46.98 (CR-LSO)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2020-06-18
    94.02
    best: 94.37 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Val)· 2020-06-18
    91.12
    best: 91.61 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2020-06-18
    71.92
    best: 73.51 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Val)· 2020-06-18
    72.12
    best: 73.49 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • AutoMLonNAS-Bench-201, ImageNet-16-120
    Accuracy (Test)· 2020-06-18
    45.51
    best: 46.98 (CR-LSO)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2020-06-18
    94.02
    best: 94.37 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Val)· 2020-06-18
    91.12
    best: 91.61 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2020-06-18
    71.92
    best: 73.51 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Val)· 2020-06-18
    72.12
    best: 73.49 (DiNAS)
    Cyclic Differentiable Architecture SearcharXiv:2006.10724