https://github.com/adrianatienza1996/DiffusionModels/tree/main/CSDI
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
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/adrienchaton/BERGAN
https://github.com/advaitrane/GANimorph_pytorch
Improving Shape Deformation in Unsupervised Image-to-Image Translation
https://github.com/advimman/lama
Resolution-robust Large Mask Inpainting with Fourier Convolutions
https://github.com/aelnouby/Text-to-Image-Synthesis
https://github.com/affinelayer/Pix2Pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/afiaka87/clip-guided-diffusion
https://github.com/afruehstueck/VIVE3D
https://github.com/afruehstueck/tileGAN
https://github.com/ag17sep/Image-Captioning-Project
https://github.com/agermanidis/pix2pixHD-runway
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/agupta231/CARROL
https://github.com/ahmad0790/svhn-multi-digit-address-recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ahmadelsallab/lidargan
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
https://github.com/ahmed-alllam/Equinox/blob/main/examples/deep_convolutional_gan.ipynb
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ahmedabdel-hady/Space-Xplores-Nasaspaceapp2020
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ahmedbesbes/cycle-gan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ahmedbesbes/whales-classification
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/ahmedbhna/VGG_Paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ahmedelmahy/myownvit
https://github.com/ahmedsalahacc/Image-Style-Transfer-with-Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ahoudard/wgenpatex
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
https://github.com/ai-forever/kandinskyvideo
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/ai-forever/movqgan
https://github.com/ai4science-westlakeu/cl_diffphycon
https://github.com/aigagror/discriminator-style-transfer
https://github.com/aigitsaj13/pix2pix-model
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/aigtext/glyphcontrol-release
https://github.com/aiinai/tf-insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/aiksir/stylegan2-ada-blending
https://github.com/aim-uofa/styledrop-pytorch
https://github.com/aimagelab/LLaVA-MORE
The Revolution of Multimodal Large Language Models: A Survey
https://github.com/aimiokab/misr-s2
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
https://github.com/airalcorn2/deformer
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
https://github.com/aitorzip/PyTorch-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ajaystar8/PDRUNet-PyTorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.