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Models/β-DARTS

β-DARTS

Reported on 14 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.

Methodology14 results

  • Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
    Accuracy (Test)· 2022-03-03
    46.34
    best: 46.98 (CR-LSO)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
    Accuracy (Val)· 2022-03-03
    46.37
    best: 46.73 (AG-Net)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2022-03-03
    94.36
    best: 94.37 (DiNAS)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Val)· 2022-03-03
    91.55
    best: 91.61 (DiNAS)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • Neural Architecture SearchonCIFAR-100
    Percentage Error· 2022-03-03
    16.52
    best: 11.7 (DNA-c)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2022-03-03
    73.51
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Val)· 2022-03-03
    73.49
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonNAS-Bench-201, ImageNet-16-120
    Accuracy (Test)· 2022-03-03
    46.34
    best: 46.98 (CR-LSO)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonNAS-Bench-201, ImageNet-16-120
    Accuracy (Val)· 2022-03-03
    46.37
    best: 46.73 (AG-Net)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2022-03-03
    94.36
    best: 94.37 (DiNAS)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Val)· 2022-03-03
    91.55
    best: 91.61 (DiNAS)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonCIFAR-100
    Percentage Error· 2022-03-03
    16.52
    best: 11.7 (DNA-c)
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2022-03-03
    73.51
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Val)· 2022-03-03
    73.49
    $β$-DARTS: Beta-Decay Regularization for Differentiable Architecture SearcharXiv:2203.01665