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/GrahamRigby/GauGanPlus
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/GrayXu/SpectralNormalization-TF-Keras
https://github.com/Graylab/deepH3-distances-orientations
https://github.com/GrigoryBartosh/neural_diffusion
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
https://github.com/Gryffindor112358/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/HFAiLab/clip-gen
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
https://github.com/HHTseng/video-classification
https://github.com/HKUNLP/Dream
https://github.com/HSabbar/Generation-lyrics-PNL
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/HUJI-Deep/nice
https://github.com/Ha0Tang/ASGAN
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/Ha0Tang/HandGestureRecognition
https://github.com/Ha0Tang/LocalGlobalGAN
https://github.com/Ha0Tang/XingVTON
https://github.com/HaijunMa/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/HaijunMa/GAN-Getting-started-learning
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Halesu/4th-ML100Days
https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
https://github.com/HannH/DMFN
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harry-Muzart/harry-muzart.github.io
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harrypotterrrr/MobileStyleGAN
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis
https://github.com/HarshaVardhanVanama/Synthetic-Medical-Images
Synthetic Medical Images from Dual Generative Adversarial Networks
https://github.com/Hayashi-Yudai/ML_models
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Hazel1994/ViPE-Videos
https://github.com/HazemLahoual/Project-Machine-Learning
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Heliang-Zheng/Style2
https://github.com/HengyiWang/neural-rgbd-torch
https://github.com/Henrygwb/conditional-domine-transfer-network
https://github.com/Heonozis/FaceShifter-pytorch
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/Herge/GAN2
https://github.com/Heumi/BEGAN-tensorflow
https://github.com/HiGal/Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/HighCWu/stylegan2-paddle
https://github.com/HilaManor/Generative-deep-features
SinGAN: Learning a Generative Model from a Single Natural Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.