https://github.com/katieluo88/staypositive
공식 코드Stay Positive: Non-Negative Image Synthesis for Augmented Reality
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/kazuto1011/dusty-gan
공식 코드https://github.com/ken-ouyang/PGMPI
공식 코드Real-Time Neural Character Rendering with Pose-Guided Multiplane Images
https://github.com/keqpan/udsr
공식 코드https://github.com/kevinhkhsu/DA_detection
공식 코드https://github.com/kgl-prml/Pixel-Level-Cycle-Association
공식 코드Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
https://github.com/kid-liet/imse
공식 코드https://github.com/kid-liet/reg-gan
공식 코드https://github.com/kregmi/cross-view-image-synthesis
공식 코드https://github.com/kunheek/style-aware-discriminator
공식 코드A Style-aware Discriminator for Controllable Image Translation
https://github.com/laxmaniron/MSG-U-Net
공식 코드Efficient High-Resolution Image-to-Image Translation using Multi-Scale Gradient U-Net
https://github.com/layumi/AdaBoost_Seg
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Cifar10-Adaboost
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Seg-Uncertainty
공식 코드Unsupervised Scene Adaptation with Memory Regularization in vivo
https://github.com/lenka98/Bind-Deblurring-using-GANs
공식 코드https://github.com/lesley222/HJAILab-OPEN
공식 코드RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
https://github.com/levente-1/GAMBAS
공식 코드GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI
https://github.com/lgraesser/im2im2im
공식 코드https://github.com/lhoyer/DAFormer
공식 코드https://github.com/lhoyer/hrda
공식 코드HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
https://github.com/lhoyer/mic
공식 코드MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
https://github.com/li-xirong/mmc-amd
공식 코드Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization
https://github.com/liaohaofu/adn
공식 코드Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
https://github.com/lidan1/PhotoSketchMAN
공식 코드High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
https://github.com/lifangda01/adaptivesupervisedpatchnce
공식 코드https://github.com/lightchaserx/neural-global-shutter
공식 코드https://github.com/linjx-ustc1106/DosGAN-PyTorch
공식 코드https://github.com/linjx-ustc1106/TuiGAN-PyTorch
공식 코드TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
https://github.com/linjx-ustc1106/ZstGAN-PyTorch
공식 코드ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
https://github.com/littlewhitesea/360pant
공식 코드360PanT: Training-Free Text-Driven 360-Degree Panorama-to-Panorama Translation
https://github.com/liyunsheng13/BDL
공식 코드Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/ljjcoder/ehtdi
공식 코드https://github.com/ls4gan/benchmarking
공식 코드UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
https://github.com/ls4gan/uvcgan
공식 코드UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
https://github.com/ls4gan/uvcgan2
공식 코드UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
https://github.com/lslrh/cpsl
공식 코드Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.