https://github.com/WeiYangze/hibernate-demo
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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/Wenchao-Du/LIR-for-Unsupervised-IR
https://github.com/WuRui-Ella/Myfirststore
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/WuZhewei/my_First_GAN_tryout
https://github.com/XJhaoren/Academic-Group
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/XavierJiezou/cloud-removal-deploy
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/XiangGao1102/CTSS
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
https://github.com/XiangGao1102/FCDiffusion_code
Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation
https://github.com/XiudingCai/PyTorch-EnCo
Contrastive Learning for Unpaired Image-to-Image Translation
https://github.com/Xnsam/clothing_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Yash-10/modified_gravity_emulation
https://github.com/Yashgh7076/CPSC-8810-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/YiteWang/CS547-final-project
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Yodai1996/CV
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/YouYueHuang/CycleGAN_Unsupervised_Domain_Adaptation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Yougunhee/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/YoungWoong-Cho/Decay
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/YuanxunLu/LiveSpeechPortraits
Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/YvanG/VQGAN-CLIP
https://github.com/ZC119/Handwritten-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ZTao-z/multiflow-resnet-ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZacharyGoshen/cifar-10-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zachdr1/CNNs
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)
https://github.com/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZhangGongjie/CycleGAN-Pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/a-martyn/unet
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/abaietto/neural_ode_classification
https://github.com/abhishtagatya/paintgan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/abods/generative_painting
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/abuchin/resnet_sign
https://github.com/aby2s/sharpmask
https://github.com/acecreamu/angularGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/acht7111020/DSMAP
Domain-Specific Mappings for Generative Adversarial Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.