Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration. ( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )
image-generation
Image-to-Image Translation
3260 papers • 126 benchmarks • 313 datasets
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in image-generation
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SYNTHIA-to-Cityscapes
SYNTHIA-to-Cityscapes
GTAV-to-Cityscapes Labels
GTAV-to-Cityscapes Labels
Libraries
iUse these libraries to find image-generation models and implementations
8 papers 15,669
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No datasets available.
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Most implemented papers
Deep Residual Learning for Image Recognition
Kaiming He, X. Zhang, Shaoqing Ren•Wed Dec 09 2015
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
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