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Reported on 8 benchmarks across 4 tasks · 1 paper · 8 SOTA

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

Computer Vision4 results

  • Image Super-ResolutiononUrban100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1007
    best: 0.0945 (AESOP)
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437
  • Image Super-ResolutiononBSD100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1209
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437
  • 3D Object Super-ResolutiononUrban100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1007
    best: 0.0945 (AESOP)
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437
  • 3D Object Super-ResolutiononBSD100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1209
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437

Graphs2 results

  • Super-ResolutiononUrban100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1007
    best: 0.0945 (AESOP)
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437
  • Super-ResolutiononBSD100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1209
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437

Methodology2 results

  • 16konUrban100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1007
    best: 0.0945 (AESOP)
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437
  • 16konBSD100 - 4x upscaling
    LPIPS· 2021-06-19
    0.1209
    SOTA
    One-to-many Approach for Improving Super-ResolutionarXiv:2106.10437