https://github.com/Proxim123/person-reID-No1-
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/PuchatekwSzortach/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/QXL4515/CAGFuzz
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Qi-Xian/color-transfer_HW1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/RAGHAV2998/Everybody-Can-Dance-Now-Video-game-version-
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/RRoundTable/OceanLitter_dataset_generator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/RanBezen/cycleGan_handwriting
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Raykoooo/IAST
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/RezisEwig/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ritam9/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ritam9/DualGAN
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
https://github.com/Rivendile/ACL-GAN
Unpaired Image-to-Image Translation using Adversarial Consistency Loss
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/Rohed/ml-1
https://github.com/Rongpeng-Lin/StarGAN-Multi-domain-use-tensorflow
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/RudreshVeerkhare/StyleGan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Rust401/pixel2piexl
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Ruturaj123/VGG16-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/S-aiueo32/dni-demo
Deep Network Interpolation for Continuous Imagery Effect Transition
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SHAHBAZSIDDIQI/DataScience
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/STASYA00/UrbanGen
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/SUPERSHOPxyz/stylegan3-gradient
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-Model-Builder-KERAS
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.