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/avivga/lord-pytorch
https://github.com/avocardio/resnet_vs_convnext
https://github.com/awesomephant/vf-installation
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/awjuliani/Pix2Pix-Film
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/axondeepseg/AxonDeepSynth
Unsupervised Medical Image Translation with Adversarial Diffusion Models
https://github.com/ayanglab/cs2
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ayanglab/hdl
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ayushdabra/drone-images-semantic-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/lunar-landscape-images-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/retinal-oct-images-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/gan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/b8goal/PIRL-Residual-network
https://github.com/bailvwangzi/repulsion_loss_ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bareeka/ganproject
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/barmayo/spatial_attention
https://github.com/bartoszpanek1/Real-time-emotion-recognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ben-davidson-6/fixup
https://github.com/bentrevett/pytorch-image-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bepierre/SpeechVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bertrandlalo/deep_dog_breeds
https://github.com/bethgelab/InDomainGeneralizationBenchmark
https://github.com/bionanoimaging/cellSTORM-Tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/blablabananarama/ukiyoGAN
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
https://github.com/boris127/vehicle-detection
https://github.com/brade31919/SRGAN-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/brycexu/MR-Residual-Net
https://github.com/buriedms/AdaptSegNet-Paddle
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/bxck75/piss-ant-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calico/scnym
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.