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Models/Linearity (Norm-in-Norm Loss)

Linearity (Norm-in-Norm Loss)

Reported on 9 benchmarks across 3 tasks · 1 paper

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

Computer Vision6 results

  • Video UnderstandingonMSU SR-QA Dataset
    KLCC· 2020-08-10
    0.52172
    best: 0.69774 (ClipIQA+)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video UnderstandingonMSU SR-QA Dataset
    PLCC· 2020-08-10
    0.62204
    best: 0.75743 (PieAPP)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video UnderstandingonMSU SR-QA Dataset
    SROCC· 2020-08-10
    0.64382
    best: 0.75215 (PieAPP)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • VideoonMSU SR-QA Dataset
    KLCC· 2020-08-10
    0.52172
    best: 0.69774 (ClipIQA+)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • VideoonMSU SR-QA Dataset
    PLCC· 2020-08-10
    0.62204
    best: 0.75743 (PieAPP)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • VideoonMSU SR-QA Dataset
    SROCC· 2020-08-10
    0.64382
    best: 0.75215 (PieAPP)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889

Time Series3 results

  • Video Quality AssessmentonMSU SR-QA Dataset
    KLCC· 2020-08-10
    0.52172
    best: 0.69774 (ClipIQA+)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video Quality AssessmentonMSU SR-QA Dataset
    PLCC· 2020-08-10
    0.62204
    best: 0.75743 (PieAPP)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video Quality AssessmentonMSU SR-QA Dataset
    SROCC· 2020-08-10
    0.64382
    best: 0.75215 (PieAPP)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889