https://github.com/NVlabs/DG-Net
Joint Discriminative and Generative Learning for Person Re-identification
Papers with Code 아카이브
Image-to-Image Translation
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- 평가표
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- 보존된 평가 기록
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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/Nadavc220/DomainAdversarialTrainingOfNeuralNetworks
https://github.com/NathanDeMaria/AugmentedCycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Negus25/MindSpore-StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/NeuralVFX/lighting-swap-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NeuralVFX/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Nguyendat-bit/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Nirav-Agarwal/GANs
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Nisnab/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/NiteshBharadwaj/adaptsegnet-materials
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/NitishaS-812k/Monet-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NiucunCode/Deep-Learning-proj2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Niwhskal/SRNet
https://github.com/Novemser/deep-imitation
https://github.com/Nyn-ynu/MCD
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/OFRIN/Tensorflow_Pixel2Pixel
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/Omkar-Ranadive/Domain-Adaptation-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Our4514/CAGFUZZ
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PDEUXA/AIF_CYCLEGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/PPPrior/i3d-pytorch
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter04
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter06
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/PaddlePaddle/PaddleClas
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/Pavan-pk/ArtGan_Realism
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Pengxiao-Wang/Typeface-and-Font-Style-Transfer
https://github.com/PeteXC/Transfer-Learning
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
https://github.com/PieterBijl/Group28
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PracNeyman/attention_guided_img_trans
https://github.com/PramuPerera/In2I
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.