https://github.com/hejj16/Landscape-StyleGAN
A Style-Based Generator Architecture for Generative Adversarial 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/hellbell/KeyPatchGan
Unsupervised Holistic Image Generation from Key Local Patches
https://github.com/hello-world-cc/starGANv1-Pytorch
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/helmy-elrais/Semi_Supervised_Learning
https://github.com/hendraet/research-GANwriting
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
https://github.com/henriquemeloo/cat-dog-image-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/henry32144/wgan-gp-tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/henryaddison/score_sde_pytorch
Generative Modeling by Estimating Gradients of the Data Distribution
https://github.com/hero9968/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/hershd23/ObjectLocalizer
https://github.com/heykeetae/Self-Attention-GAN
https://github.com/hg1722/fashionista
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/hichoe95/rarity-score
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
https://github.com/hidekifujinami6/gan_evaluation
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/hieu2906090/deep-learning-in-js
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hinofafa/Self-Attention-GAN
https://github.com/hinofafa/Self-Attention-HearthStone-GAN
https://github.com/hiroyasuakada/StyleGAN2-PyTorch
https://github.com/hiyouga/SAGAN-PyTorch
https://github.com/hmatty/stylized-neural-painting
https://github.com/hojonathanho/localbitsback
https://github.com/hollance/VGGNet-Metal
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hollygrimm/cyclegan-keras-art-attrs
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/holstgr-kaust/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/homebrewltd/ichigo
https://github.com/hongkunsun/paratranscnn
https://github.com/hongpeng-guo/deep-image-prior
https://github.com/hongyi-zhang/Fixup
https://github.com/honzaMaly/cyclegan-style-transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/horenbergerb/BernoulliDiffusion
https://github.com/horse007666/ResNet
https://github.com/hotfinda/tcnet
https://github.com/hqhoangvuong/paperspace_ml_01
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/hqng/imm-pytorch
Unsupervised Learning of Object Landmarks through Conditional Image Generation
https://github.com/hrbigelow/draw
https://github.com/hsd1503/resnet1d
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.