High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic... (read more)

PDF Abstract CVPR 2018 PDF CVPR 2018 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image-to-Image Translation ADE20K Labels-to-Photos pix2pixHD mIoU 20.3 # 6
Accuracy 69.2% # 4
FID 81.8 # 7
Image-to-Image Translation ADE20K-Outdoor Labels-to-Photos pix2pixHD mIoU 17.4 # 3
Accuracy 71.6% # 3
FID 97.8 # 5
Image-to-Image Translation Cityscapes Labels-to-Photo pix2pixHD Per-pixel Accuracy 81.4% # 4
mIoU 58.3 # 5
FID 95 # 7
Fundus to Angiography Generation Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients pix2pixHD FID 42.8 # 6
Kernel Inception Distance 0.00258 # 5

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Image-to-Image Translation COCO-Stuff Labels-to-Photos pix2pixHD mIoU 14.6 # 5
Accuracy 45.8% # 3
FID 111.5 # 5

Methods used in the Paper


METHOD TYPE
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