Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Perceptual Loss

Perceptual Loss

Reported on 8 benchmarks across 4 tasks · 1 paper

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

Computer Vision4 results

Image Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2016-03-27
24.95
best: 28.16 (DRCT-L)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155
Image Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2016-03-27
0.6317
best: 0.851 (Edge-informed SR)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155
3D Object Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2016-03-27
24.95
best: 28.16 (DRCT-L)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155
3D Object Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2016-03-27
0.6317
best: 0.851 (Edge-informed SR)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155

Graphs2 results

Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2016-03-27
24.95
best: 28.16 (DRCT-L)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155
Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2016-03-27
0.6317
best: 0.851 (Edge-informed SR)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155

Methodology2 results

16konBSD100 - 4x upscaling
PSNR· 2016-03-27
24.95
best: 28.16 (DRCT-L)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155
16konBSD100 - 4x upscaling
SSIM· 2016-03-27
0.6317
best: 0.851 (Edge-informed SR)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution arXiv:1603.08155