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Models/DAC

DAC

Reported on 13 benchmarks across 1 task

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

Computer Vision13 results

  • Image ClusteringonImageNet-10
    Accuracy
    0.527
    best: 0.992 (TAC)
  • Image ClusteringonImageNet-10
    NMI
    0.394
    best: 0.985 (TAC)
  • Image ClusteringonCIFAR-10
    ARI· uses extra data
    0.301
    best: 0.989 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonCIFAR-10
    Accuracy· uses extra data
    0.522
    best: 0.995 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonCIFAR-10
    NMI· uses extra data
    0.4
    best: 0.985 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonTiny-ImageNet
    Accuracy
    0.066
    best: 0.698 (PRO-DSC)
  • Image ClusteringonTiny-ImageNet
    NMI
    0.19
    best: 0.8178 (ITAE)
  • Image ClusteringonCIFAR-100
    Accuracy· uses extra data
    0.238
    best: 0.898 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonCIFAR-100
    NMI· uses extra data
    0.185
    best: 0.915 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonSTL-10
    Accuracy· uses extra data
    0.47
    best: 0.997 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonSTL-10
    NMI· uses extra data
    0.366
    best: 0.993 (TURTLE (CLIP + DINOv2))
  • Image ClusteringonImagenet-dog-15
    Accuracy
    0.275
    best: 0.943 (MAE-CT (best))
  • Image ClusteringonImagenet-dog-15
    NMI
    0.219
    best: 0.904 (MAE-CT (best))