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/NMADALI97/Learning-With-Wasserstein-Loss
https://github.com/NVIDIA/UnsupervisedLandmarkLearning
Unsupervised Part-Based Disentangling of Object Shape and Appearance
https://github.com/NVIDIA/retinanet-examples
https://github.com/NVlabs/DG-Net
Pose-Normalized Image Generation for Person Re-identification
https://github.com/NVlabs/eg3d
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/NVlabs/stylegan2-ada-pytorch
https://github.com/Naagar/Glow_NormalizingFlow_implimentation
https://github.com/Nadavc220/DomainAdversarialTrainingOfNeuralNetworks
https://github.com/NadimKawwa/DCGAN_faces
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/NariMo91/GANs-generative-art
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/NathanDeMaria/AugmentedCycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Natsu6767/DCGAN-PyTorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Natsu6767/Generating-Devanagari-Using-DRAW
https://github.com/Natsu6767/InfoGAN-PyTorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/NawafAlsuwailem/artathon_code
https://github.com/Negus25/MindSpore-StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/Negus25/Mindspore-wgan_gp
https://github.com/Neptune-Trojans/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Net-Mist/style-transfer-tf2
Instance Normalization: The Missing Ingredient for Fast Stylization
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/NeuralVFX/wasserstein-gan
https://github.com/Newmu/dcgan_code
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Neznakomec/ml-project-transformers
https://github.com/Nguyendat-bit/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/NiceRingNode/Inpainting-Cpp-Deployment
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/NicholasKX/ShowAndTell
https://github.com/NicicDamjan/SoftKompjuting
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/NickKaparinos/Google-Landmark-Recognition-2021
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/NicolaBernini/PaperAnalysis_ICLR2021_NegativeDataAugmentation
https://github.com/NikhitaMethwani/Deep-Fakes-Cars-Generation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Nikolai10/FSQ
https://github.com/NilsBochow/lama_reconstruction
Resolution-robust Large Mask Inpainting with Fourier Convolutions
https://github.com/Nirav-Agarwal/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Nisnab/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.