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Models/FACT

FACT

Reported on 41 benchmarks across 10 tasks · 3 papers · 18 SOTA

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

Methodology17 results

  • Domain AdaptationonUSPS-to-MNIST
    Accuracy· 2023-06-01
    98.6
    best: 98.75 (FAMCD)
    SOTA
    FACT: Federated Adversarial Cross TrainingarXiv:2306.00607
  • Domain AdaptationonMNIST-to-USPS
    Accuracy· 2023-06-01
    98.8
    SOTA
    FACT: Federated Adversarial Cross TrainingarXiv:2306.00607
  • Continual LearningonCIFAR-100
    Average Accuracy· 2022-03-14
    62.24
    best: 88.08 (PriViLege)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Continual LearningonCIFAR-100
    Last Accuracy· 2022-03-14
    52.1
    best: 86.06 (PriViLege)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Continual Learningonmini-Imagenet
    Average Accuracy· 2022-03-14
    60.7
    best: 95.27 (PriViLege)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Continual Learningonmini-Imagenet
    Last Accuracy · 2022-03-14
    50.49
    best: 96.24 (CoACT)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Class Incremental LearningonCIFAR-100
    Average Accuracy· 2022-03-14
    62.24
    best: 88.08 (PriViLege)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Class Incremental LearningonCIFAR-100
    Last Accuracy· 2022-03-14
    52.1
    best: 86.06 (PriViLege)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Class Incremental Learningonmini-Imagenet
    Average Accuracy· 2022-03-14
    60.7
    best: 95.27 (PriViLege)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Class Incremental Learningonmini-Imagenet
    Last Accuracy · 2022-03-14
    50.49
    best: 96.24 (CoACT)
    SOTA
    Forward Compatible Few-Shot Class-Incremental LearningarXiv:2203.06953
  • Domain AdaptationonSVHN-to-MNIST
    Accuracy· 2023-06-01
    90.6
    best: 99.18 (Mean teacher)
    FACT: Federated Adversarial Cross TrainingarXiv:2306.00607
  • Domain AdaptationonVeri-776 to VehicleID Large
    R-1
    39.91
    best: 48.62 (CORE-ReID V2)
  • Domain AdaptationonVeri-776 to VehicleID Large
    R-5
    60.49
    best: 68.3 (CORE-ReID V2)
  • Domain AdaptationonVeri-776 to VehicleID Small
    R-1
    49.53
    best: 58.32 (CORE-ReID V2)
  • Domain AdaptationonVeri-776 to VehicleID Small
    R-5
    67.96
    best: 76.51 (CORE-ReID V2)
  • Domain AdaptationonVeri-776 to VehicleID Medium
    R-1
    44.63
    best: 53.49 (CORE-ReID V2)
  • Domain AdaptationonVeri-776 to VehicleID Medium
    R-5
    64.19
    best: 74.36 (CORE-ReID V2)

Computer Vision12 results

  • Pose TrackingonFineDance
    BAS· 2021-01-21
    0.1831
    best: 0.2423 (Lodge++)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779
  • Pose TrackingonFineDance
    fid_k· 2021-01-21
    113.38
    best: 19.36 (DanceMosaic)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779
  • Action LocalizationonGTEA
    Acc
    84.5
    best: 89.8 (Semantic2Graph)
  • Action LocalizationonGTEA
    Edit
    93.5
  • Action LocalizationonGTEA
    F1@10%
    96.1
  • Action LocalizationonGTEA
    F1@25%
    95.6
  • Action LocalizationonGTEA
    F1@50%
    87.5
    best: 91.3 (Semantic2Graph)
  • Action SegmentationonGTEA
    Acc
    84.5
    best: 89.8 (Semantic2Graph)
  • Action SegmentationonGTEA
    Edit
    93.5
  • Action SegmentationonGTEA
    F1@10%
    96.1
  • Action SegmentationonGTEA
    F1@25%
    95.6
  • Action SegmentationonGTEA
    F1@50%
    87.5
    best: 91.3 (Semantic2Graph)

Other6 results

  • Unsupervised Domain AdaptationonVeri-776 to VehicleID Large
    R-1
    39.91
    best: 48.62 (CORE-ReID V2)
  • Unsupervised Domain AdaptationonVeri-776 to VehicleID Large
    R-5
    60.49
    best: 68.3 (CORE-ReID V2)
  • Unsupervised Domain AdaptationonVeri-776 to VehicleID Small
    R-1
    49.53
    best: 58.32 (CORE-ReID V2)
  • Unsupervised Domain AdaptationonVeri-776 to VehicleID Small
    R-5
    67.96
    best: 76.51 (CORE-ReID V2)
  • Unsupervised Domain AdaptationonVeri-776 to VehicleID Medium
    R-1
    44.63
    best: 53.49 (CORE-ReID V2)
  • Unsupervised Domain AdaptationonVeri-776 to VehicleID Medium
    R-5
    64.19
    best: 74.36 (CORE-ReID V2)

Audio4 results

  • 10-shot image generationonFineDance
    BAS· 2021-01-21
    0.1831
    best: 0.2423 (Lodge++)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779
  • 10-shot image generationonFineDance
    fid_k· 2021-01-21
    113.38
    best: 19.36 (DanceMosaic)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779
  • 3D Human Pose TrackingonFineDance
    BAS· 2021-01-21
    0.1831
    best: 0.2423 (Lodge++)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779
  • 3D Human Pose TrackingonFineDance
    fid_k· 2021-01-21
    113.38
    best: 19.36 (DanceMosaic)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779

Computer Code2 results

  • Motion SynthesisonFineDance
    BAS· 2021-01-21
    0.1831
    best: 0.2423 (Lodge++)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779
  • Motion SynthesisonFineDance
    fid_k· 2021-01-21
    113.38
    best: 19.36 (DanceMosaic)
    SOTA
    AI Choreographer: Music Conditioned 3D Dance Generation with AIST++arXiv:2101.08779