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/winycg/MCL-OKD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wjy5446/pytorch-everybody-dance-now
https://github.com/wkcn/UGATIT-mxnet
https://github.com/wkhademi/ImageEnhancement
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/wkostuch/wild-style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wljungbergh/neuroncap
https://github.com/wonbeomjang/cyclegan-pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/wondonghyeon/protest-detection-violence-estimation
https://github.com/woongjoonchoi/DeepLearningPaper-Reproducing/tree/master/Vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wuqiyao20160118/mini_project-for-CIFAR10
https://github.com/wwwAych/AutoVideoColor
https://github.com/wxb589/Person_reID_baseline_pytorch-master
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xhlulu/arxiv-assistant
https://github.com/xhujoy/CycleGAN-tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xiaoiker/meta_dpm
https://github.com/xiaowillow/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/xiaowillow/AdaptSegNet1
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xingchenzhao/Generating-Human-Skeletons-with-Mutual-Actions-WGAN-Pytorch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/xingchenzhao/deep-learning-team-project
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/new_papers_codes/tree/main/Pix2PixHD
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/papers_with_examples/tree/main/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xiuyu0000/papers_with_examples/tree/main/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xw-hu/Mask-ShadowGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xxlya/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.