https://github.com/enstan/pix2pix_h5-
Image-to-Image Translation with Conditional 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/envision-research/lotus
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
https://github.com/enzomuschik/distilfnd
https://github.com/epochlab/xres
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/eps696/stargan2
https://github.com/eps696/stylegan2
https://github.com/eps696/stylegan2ada
https://github.com/erenovic/SADIM
https://github.com/eric-erki/IdenProf
https://github.com/eric-erki/insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/eric-yim/distribution_reconstruction
https://github.com/eric-yim/image_reconstruction
https://github.com/eric-yim/vqvae
https://github.com/ericjang/draw
https://github.com/ericl122333/patchdiffusion-pytorch
Progressive Distillation for Fast Sampling of Diffusion Models
https://github.com/erictzeng/adda
https://github.com/eridgd/WCT-TF
https://github.com/erik-buchholz/SoK-TrajGen
https://github.com/erikakaeri/StyleGAN2
https://github.com/erikakaeri/asdf
https://github.com/eriklindernoren/Keras-GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/eriklindernoren/PyTorch-GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/erlendd/ddan
https://github.com/ermolenkodev/da-ssd
https://github.com/ermongroup/a-nice-mc
https://github.com/ermongroup/alignflow
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
https://github.com/ermongroup/csdi
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
https://github.com/esimpsontheartist/stylegan2-FineArt
https://github.com/esslushy/AbstractArtGenerator
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/etarthur/Outpainting
Image Outpainting and Harmonization using Generative Adversarial Networks
https://github.com/etotheipi/nvae_tensorflow
https://github.com/etttttte/mayfest2018
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/eugeneteoh/greenaug
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
https://github.com/euiyulsong/Spectral-Normalization-for-GANs
https://github.com/evan11401/CS_IOC5008_0856043_HW2
https://github.com/ewinata/CIFAR-10
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.