https://github.com/SalesforceAIResearch/DiffusionDPO
Diffusion Model Alignment Using Direct Preference Optimization
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/SamArgt/AudioSourceSep
https://github.com/SamsungSAILMontreal/ForestDiffusion
https://github.com/Sangkwun/Resnet
https://github.com/Sangohe/pgan_thesis
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/SaoYan/GenerativeSkinLesion
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/SarderLab/WSI-cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/SathwikTejaswi/Neural-Image-Captioning
https://github.com/Sayak007/Text-to-Image-Synthesis-using-DCGAN
https://github.com/SayedNadim/Free_Form_Image_Inpainting_Gated_Conv
https://github.com/SayedNadim/Gated_Convolution_Tensorflow
https://github.com/SayedNadim/Global-and-Local-Attention-Based-Free-Form-Image-Inpainting
https://github.com/Se-Hun/GAN_Study
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Sean-Hastings/WGAN
https://github.com/Seed3D/Dora
https://github.com/SeitaroShinagawa/chainer-partial_convolution_image_inpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/SeniorDev009/ONNX-project
https://github.com/SeonbeomKim/TensorFlow-InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/SeonbeomKim/TensorFlow-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/SeonghoBaek/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SerezD/NVAE-from-scratch
https://github.com/SethEBaldwin/Resnet
https://github.com/Seung-Hun-Lee/ADAS
https://github.com/SevenZhan/Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Sezoir/DCGAN-Dog-Generator
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/SforAiDl/CountCLIP
https://github.com/Shalev-Lifshitz/STEVE-1
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
https://github.com/ShaoTengLiu/Video-P2P
https://github.com/ShapeNet/RenderForCNN
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
https://github.com/ShaunBarry/wavegan
https://github.com/ShawnNew/shape-alpha-matting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ShenYujun/HiGAN
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis
https://github.com/ShenYujun/InterFaceGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ShiboXing/Improved-3dbraingen
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
https://github.com/ShiboXing/Light-3dbraingen
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.