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Models/NAS-LID+RSPS

NAS-LID+RSPS

Reported on 8 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.

Methodology8 results

  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2022-11-23
    92.9
    best: 94.37 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-10
    Accuracy (Val)· 2022-11-23
    89.74
    best: 91.61 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2022-11-23
    69.39
    best: 73.51 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • Neural Architecture SearchonNAS-Bench-201, CIFAR-100
    Accuracy (Val)· 2022-11-23
    69.38
    best: 73.49 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Test)· 2022-11-23
    92.9
    best: 94.37 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • AutoMLonNAS-Bench-201, CIFAR-10
    Accuracy (Val)· 2022-11-23
    89.74
    best: 91.61 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Test)· 2022-11-23
    69.39
    best: 73.51 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759
  • AutoMLonNAS-Bench-201, CIFAR-100
    Accuracy (Val)· 2022-11-23
    69.38
    best: 73.49 (DiNAS)
    NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionarXiv:2211.12759