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/khawajatalhahaseeb/Class-Activation-Map-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/khurram702/StyleBasedGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/khush3/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/kingcheng2000/GAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/kingcong/gpu_FaceAttribute
https://github.com/kingcong/gpu_SE-Net
https://github.com/kingcong/resnet
https://github.com/kiranu1024/deep-learning
https://github.com/kishanAk21/Skin-Cancer-Image-Classification
https://github.com/kishorkuttan/traffic_net
https://github.com/kiyohiro8/pytorch-unpaired-image-to-image-translation
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/km1414/CNN-models
https://github.com/kmkwon94/ainized-ganilla-dev
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
https://github.com/kmzzhang/nbi
https://github.com/komeiharada/learn_DL_together
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/konny0311/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/konstantinos-p/image_classification_SOTA
https://github.com/koshian2/ResNet-MultipleFramework
https://github.com/kregmi/cross-view-image-matching
Cross-view image synthesis using geometry-guided conditional GANs
https://github.com/kreimanlab/HumanIntentionInferenceZeroShot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/krishnakarthi/COVID-19_Prediction
https://github.com/krumo/Detectron-DA-Faster-RCNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/krumo/Domain-Adaptive-Faster-RCNN-PyTorch
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/kryvosheyev/xray-anomaly-detection
https://github.com/kuangzijian/drifu-for-animals
https://github.com/kulkarnikeerti/Image-to-image-translation-using-cGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/kw01sg/crda
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/kylelo/roofdiffusion
https://github.com/kynkaat/improved-precision-and-recall-metric
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kzkymur/transition
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lab-midas/med_segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.