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Image To Image Translation On Cityscapes

Metrics

FID
Per-pixel Accuracy
mIoU

Results

Performance results of various models on this benchmark

Paper Title
CRN104.777.1%52.4Photographic Image Synthesis with Cascaded Refinement Networks
pix2pixHD9581.4%58.3High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Pix2PixHD-AUG72.7-58Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis
SPADE71.881.9%62.3Semantic Image Synthesis with Spatially-Adaptive Normalization
SB-GAN60.39--Semantic Bottleneck Scene Generation
SPADE + FFL59.582.5%64.2Focal Frequency Loss for Image Reconstruction and Synthesis
CC-FPSE54.382.3%65.5Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
SPADE + SESAME54.282.5%66SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
USIS53.67-44.78USIS: Unsupervised Semantic Image Synthesis
CC-FPSE-AUG52.1-63.1Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis
USIS-Wavelet50.14-42.32Wavelet-based Unsupervised Label-to-Image Translation
SIMS49.775.5%47.2Semi-parametric Image Synthesis
OASIS47.7-69.3You Only Need Adversarial Supervision for Semantic Image Synthesis
DP-GAN44.1-73.6Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis
DP-SIMS (ConvNext-L)38.2-76.3Unlocking Pre-trained Image Backbones for Semantic Image Synthesis
INADE---Diverse Semantic Image Synthesis via Probability Distribution Modeling
pix2pix-71.0-Image-to-Image Translation with Conditional Adversarial Networks
BiGAN-19%-Adversarially Learned Inference
CoGAN-40%-Coupled Generative Adversarial Networks
SimGAN-20%-Learning from Simulated and Unsupervised Images through Adversarial Training
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