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/jbornschein/draw
https://github.com/jcchiba/kamonData
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/jcchiba/sanseriffontdatasetandmodel
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/jcjohnson/fast-neural-style
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/jday96314/Kuzushiji
https://github.com/jdbugs/stylegan2
https://github.com/jdbugs/stylegan2encoder
https://github.com/jdfxzzy/pean
Text Image Inpainting via Global Structure-Guided Diffusion Models
https://github.com/jdrego/EEE511ANC-Text2ImageGAN
https://github.com/jeanjerome/semisupervised_timeseries_infogan
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/jejjohnson/rbig
https://github.com/jelifysh/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jelifysh/Image-Captioning
https://github.com/jenyliu/DLA_interview
https://github.com/jeremyiv/diffc
https://github.com/jeremyt19115678/Average-Encoding-Model
NeuroGen: activation optimized image synthesis for discovery neuroscience
https://github.com/jeromerony/Progressive_Growing_of_GANs-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jerryyann/dpi
https://github.com/jesperhauch/polynomial_deep_learning
https://github.com/jesse1029/Fake-Face-Images-Detection-Tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jessica3724/Keras_VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jessiemjones/TF-2.0-Neural-Style-Transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jfcrenshaw/pzflow
https://github.com/jhaux/VUNet
A Variational U-Net for Conditional Appearance and Shape Generation
https://github.com/jhayes14/advsteg
https://github.com/jhchang/DFDC
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/jher123/GAN-experiments
https://github.com/jher123/WGAN-experiments
https://github.com/jhjacobsen/invertible-resnet
https://github.com/jia-yi-chen/Illumination-guided-Neural-Style-Transfer
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/jiahuadong/cifc
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/jiajunhua/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jiajunhua/JiahuiYu-generative_inpainting
https://github.com/jiajunhua/dcgan_code
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jiajunhua/facebookresearch-Detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.