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/anjalipemmaraju/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/anjandeepsahni/face_classification
https://github.com/anjandeepsahni/face_recognition
https://github.com/ankit-vaghela30/Google-landmark-prediction
https://github.com/anoushkrit/Knowledge
https://github.com/antoinedemathelin/wann
https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/anuraggoel70/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/aobaruwa/TensorFlow-Projects
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/apple/ml-cvpr2019-swd
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
https://github.com/arahosu/musicslots
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ariangc/breinchallenge
https://github.com/arnab39/cycleGAN-PyTorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavdodiedo/dog2sketch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavdodiedo/sketch2face
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arobey1/mbrdl
https://github.com/asahi417/DeepDomainAdaptation
https://github.com/asarigun/MixerGANsformer
MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation
https://github.com/asbjrnmunk/mdd-unet
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras
https://github.com/ashkanpakzad/atn
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ashura1234/soccer-line-detection
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ashushekar/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ashutosh1919/FaceGenerationStyleGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/asmerdov/DataCollectionSystem
https://github.com/asprenger/keras_fc_densenet
https://github.com/asraman9792/Generative-Adversarial-Networks-implementation-of-KERAS
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/astoff/dog-breed-app
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/astoycos/Mini_Project2
https://github.com/astro-informatics/rcGAN
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
https://github.com/asyrovprog/cs230project
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/attm/tensorflow_advent
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/avellal14/AI-for-Art
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.