Papers with Code 아카이브
Image-to-Image Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 38
- 보존된 평가 기록
- 195
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/Sakib1263/VGG-1D-2D-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-KERAS
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sangkwun/Resnet
https://github.com/SarderLab/WSI-cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/SeniorDev009/ONNX-project
https://github.com/SeonbeomKim/TensorFlow-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/SeonghoBaek/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SethEBaldwin/Resnet
https://github.com/Seung-Hun-Lee/ADAS
https://github.com/ShawnNew/shape-alpha-matting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ShiboXing/Improved-3dbraingen
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
https://github.com/ShiboXing/Light-3dbraingen
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
https://github.com/ShichengChen/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/ShigemichiMatsuzaki/MSPL
https://github.com/Shilin-LU/TF-ICON
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
https://github.com/Shivamt99/Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Shumway82/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/Siddhartha24795/Medical-Image-Synthesis
https://github.com/SimeonKraev/Musical-style-transfer-with-cycle-consistent-generative-adversarial-neural-networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Smilels/multimodal-translation-teleop
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
https://github.com/Solacex/CCM
https://github.com/Sontref/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SoonminHwang/rgbt-ped-detection
https://github.com/Spoorthy-gunda/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sshanu/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/Stevan-Zhuang/image-domain-transfer
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/Sudhir11292rt/myBDL
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/SummerHuiZhang/StarGAN_Norland
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/SummerHuiZhang/StarGAN_test
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/SupratimH/neural-style-transfer
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.