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/joeynavarro/facial_expression_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/joh-fischer/PlantLDM
https://github.com/johngear/eecs504
https://github.com/johnnylu305/resnet-50-101-152
https://github.com/jonryf/deep-learning-image-colorization-using-gan
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jonvthvn90/GitHbProject
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/jooyae/NVIDIA_STYLEGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jordan-bird/synthetic-fruit-image-generator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/json9512/sonarToimage
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jtcass01/SIMON
https://github.com/jtiger958/cyclegan-pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jtiger958/pytorch-computer-vision-basic
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtiger958/pytorch-computer-vision-tutorial
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/julik43/IIMAS-USCS
https://github.com/jung-jun-uk/mixface
https://github.com/junzhin/dgm-vlc
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/justinbellucci/cnn-visualizations-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/justinjohn0306/EverybodyDanceNow-Colab
https://github.com/justusschock/delira_cycle_gan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jvanvugt/pytorch-domain-adaptation
https://github.com/jvoynow/ConvNet-Architectures
https://github.com/k-miran/hear
https://github.com/kaist-dmlab/SELFIE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kakaoenterprise/kakaoenterprise.github.io
A Plug-in Method for Representation Factorization in Connectionist Models
https://github.com/kamanphoebe/motiondetection
https://github.com/kanhereashwin/cs236-project
https://github.com/kaonashi-tyc/zi2zi
https://github.com/karl-hajjar/Generative-Adversarial-Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/karlchahine/neural-cover-selection-for-image-steganography
https://github.com/kaseris/ILSVRCPlus
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kbpi314/iclust_paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kclip/localized-adaptive-risk-control
https://github.com/keillernogueira/exploit-cnn-rs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/keras-team/keras-applications
https://github.com/kevin851066/Deep_Learning_for_Computer_Vision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.