https://github.com/VSAnimator/stgan
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/VachanVY/diffusion-transformer
https://github.com/Vaibhav-nn/EdgeConnect-Inpainting
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/Vaibhav-nn/picTranslate
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/Vakihito/SentimentYoutube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Vamshi399/CarND-Vehicle-Detection
https://github.com/VamsiKrishna1211/gender_age-detection
https://github.com/VedantDere0104/CoMo_GAN
https://github.com/VedantDere0104/Cycle_GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/VedantDere0104/Pix2Pix_GAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/VedantDere0104/Progressive_Growing_of_GANs
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/VedantDere0104/StyleGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Venkateshwar2506/WGAN---PyTorch-implementation
https://github.com/VhalPurohit/290s
https://github.com/Vigneshthanga/stackGAN-v2
https://github.com/Vill-Lab/forkOfStyleShot
https://github.com/VinayBN8997/ResNet-CIFAR10
https://github.com/Vinayakam19/Deep-Convolution-Generative-Adversarial-Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Vinayakam19/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/VincieD/TrafficSign_GAN_Benchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/VinitSR7/Image-Caption-Generation
https://github.com/Vious/LBAM_Pytorch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Vishal-V/GSoC
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/Vishal-V/GSoC-TensorFlow
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/Vishal-V/StackGAN
https://github.com/VisiumCH/AMLD2020-Dirty-Gancing
https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet
https://github.com/VitoRazor/Gan_Architecture
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/Vivswan/ECE-3195-Course-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/VladAndronik/face_swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/VladAndronik/shadow-gans
https://github.com/Vladislav-IS/dls_project_pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/VyshnaviSK/Face2Mask-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/WANG-KX/SIREN-2D
Implicit Neural Representations with Periodic Activation Functions
https://github.com/WGLab/SGAN
https://github.com/WangZesen/Spectral-Normalization-GAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.