https://github.com/ajbrock/BigGAN-PyTorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
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/ajdillhoff/simgan-pytorch
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ajithvallabai/getsetgo_keras-beginner
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akamaster/pytorch_resnet_cifar10
https://github.com/akanametov/PathGAN
https://github.com/akanametov/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/akanimax/BMSG-GAN
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
https://github.com/akanimax/T2F
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/akanimax/Variational_Discriminator_Bottleneck
https://github.com/akanimax/attn_gan_pytorch
https://github.com/akanimax/dcgan_pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/akanimax/pro_gan_pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akashgit/stylegan2_wt
https://github.com/akashpalrecha/deblurring-pipeline
https://github.com/akashpalrecha/superres-deformable
https://github.com/akashsonowal/ddpo-pytorch
https://github.com/akatigre/multi2one
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/akhadangi/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akjgskwi/DCGAN_implement
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/akommini/Spoken-Numeric-Digit-detection
https://github.com/akshay-gupta123/WGAN
https://github.com/akuramshin/cDCGAN-Quickdraw
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/alalagong/LEDNet
https://github.com/alan-chengzhang/GAN_Advance-Machine-Learning
https://github.com/albarji/toy-diffusion
https://github.com/albertomontesg/keras-model-zoo
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alectryonexamples/gan_tf2
https://github.com/alecwangcq/KFAC-Pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aleksey-egorov/attngan
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/alekseynp/stylegan2-pytorch
https://github.com/aleph-alpha/multifusion
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
https://github.com/alex-f1tor/Image-Caption
https://github.com/alex98chen/testGAN
https://github.com/alexandervnikitin/tsgm
https://github.com/alexanderzyl/stargan
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.