Papers with Code 아카이브
Image-to-Image Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 38
- 보존된 평가 기록
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/isrugeek/semcolour
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/itijyou/ademxapp
https://github.com/itsss/ganimorph
Improving Shape Deformation in Unsupervised Image-to-Image Translation
https://github.com/itsuki8914/CycleGAN-TF
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/itsuki8914/pix2pix-automaticColorization
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/itsuki8914/starGAN-LSGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/ivankunyankin/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/iver56/automatic-video-colorization
https://github.com/ivychill/reid
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/j-nowak/covid-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/j-void/ISL_v2v
https://github.com/j3soon/arxiv-utils
https://github.com/jackaduma/CycleGAN-VC2
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jackyjsy/SGGAN
Segmentation Guided Image-to-Image Translation with Adversarial Networks
https://github.com/jaingaurav3/GAN-Hacks
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/james77777778/keras-image-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jameswang287/Car-Detection
https://github.com/janZub-AI/EmotionDetectionVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/janfreyberg/pytorch-revgrad
https://github.com/jason9693/cycleGAN-tensorflow-v2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jayChung0302/DeepFilter
https://github.com/jcchiba/kamonData
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/jcchiba/sanseriffontdatasetandmodel
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/jday96314/Kuzushiji
https://github.com/jessica3724/Keras_VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jessiemjones/TF-2.0-Neural-Style-Transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiajunhua/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jiajunhua/facebookresearch-Detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiangsikai/Person_reID_baseline_pytorch
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jinlong17/da-detect
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/jiupinjia/stylized-neural-painting
https://github.com/jiweibo/imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jizongFox/ReproduceAdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/jm199504/VGG-NET
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.