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Models/GAEA DARTS (ERM)

GAEA DARTS (ERM)

Reported on 6 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Methodology6 results

  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2020-04-16
    94.1
    best: 94.37 (DiNAS)
    SOTA
    Geometry-Aware Gradient Algorithms for Neural Architecture SearcharXiv:2004.07802
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2020-04-16
    73.43
    best: 73.51 (DiNAS)
    SOTA
    Geometry-Aware Gradient Algorithms for Neural Architecture SearcharXiv:2004.07802
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2020-04-16
    94.1
    best: 94.37 (DiNAS)
    SOTA
    Geometry-Aware Gradient Algorithms for Neural Architecture SearcharXiv:2004.07802
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2020-04-16
    73.43
    best: 73.51 (DiNAS)
    SOTA
    Geometry-Aware Gradient Algorithms for Neural Architecture SearcharXiv:2004.07802
  • Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
    Accuracy (Test)· 2020-04-16
    46.36
    best: 46.98 (CR-LSO)
    Geometry-Aware Gradient Algorithms for Neural Architecture SearcharXiv:2004.07802
  • AutoMLonNAS-Bench-201, ImageNet-16-120
    Accuracy (Test)· 2020-04-16
    46.36
    best: 46.98 (CR-LSO)
    Geometry-Aware Gradient Algorithms for Neural Architecture SearcharXiv:2004.07802