https://github.com/duxingren14/Hifill-tensorflow
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/dv-fenix/Domain-Adaptation
https://github.com/dwen3232/op-colorization
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/dylanell/conditional-wgan
https://github.com/dylanell/wasserstein-gan
https://github.com/dzq84/MusicScore-script
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/e-271/few-shot-gan
https://github.com/e-hulten/made
https://github.com/e-hulten/real-nvp-2d
https://github.com/e-hulten/real_nvp_2d
https://github.com/e4s2022/e4s
https://github.com/easternCar/Face-Parsing-Network
https://github.com/eclipse-t2i/lambda-eclipse-inference
https://github.com/ecmeldmt/AttentionGAN-v0
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/ecovision-uzh/climplicit
Implicit Neural Representations with Periodic Activation Functions
https://github.com/edapinenut/ppflow
https://github.com/edward3862/CariMe-pytorch
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
https://github.com/edward3862/carime
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
https://github.com/ee17b031-iittp/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/eecsdanny/AFAD_DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/eggplant60/dcgan_anime
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ehsannowroozi/RDFS
https://github.com/eify/open_clip
LAION-5B: An open large-scale dataset for training next generation image-text models
https://github.com/eitan3/siren_gluon
Implicit Neural Representations with Periodic Activation Functions
https://github.com/eitanrich/torch-mfa
https://github.com/eladrich/pix2vertex.pytorch
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
https://github.com/eleGAN23/QVAE
https://github.com/elingaard/infogan-mnist
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/elliottwu/stext2image
Image Generation from Sketch Constraint Using Contextual GAN
https://github.com/elsa9421/cyclegan_readable
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/elvisyjlin/RelGAN-PyTorch
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
https://github.com/elybecker/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/emanuelbodin/hologan-2.0
HoloGAN: Unsupervised learning of 3D representations from natural images
https://github.com/endywon/texture-reformer
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.