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/google/sg2im
https://github.com/gopi-kampli/ImageClusteringDL
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gordicaleksa/pytorch-gans
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/gori1x62/StyleGAN2
https://github.com/gpleiss/limits_of_large_width
https://github.com/gpspelle/Crowd-Counting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/greatsharma/DeepLearning-Papers-Implementation
https://github.com/gregoiredervaux/face_recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/greshake/lm-safety
https://github.com/grey-eye/talking-heads
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/griffinbran/machines_best_friend
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/grip-unina/m3dsynth
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations
https://github.com/grunnery/StyleGAN2
https://github.com/gtegner/mine-pytorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/gtegner/real_nvp
https://github.com/guguguzi/PixelCNN-Paddle
https://github.com/guillaumeboniface/super_resolution
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/gulperii/githubio
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
https://github.com/gulshanBakle/Neural-Image-Caption-generator
https://github.com/gundeep15/SCD_AI_Project
https://github.com/gunpowder1473/CycleGan_Tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/gunpowder1473/CycyleGan_Tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/guojunq/lsgan
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
https://github.com/guptag22/uic-cs512-project
https://github.com/guptakhil12/show-tell
https://github.com/gurkirt/realtime-action-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/guy-oren/DIRT-OST
https://github.com/guy-oren/OneShotTranslationExt
https://github.com/gzhu06/MCG-ipynb
Improving Diffusion Models for Inverse Problems using Manifold Constraints
https://github.com/haaz77/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hadi-hosseini/noise-refinement
Fine-Grained Alignment and Noise Refinement for Compositional Text-to-Image Generation
https://github.com/hahahappyboy/GANForCartoon
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/haiv-lab/ospcoop_imagenet-bg
Resolution-robust Large Mask Inpainting with Fourier Convolutions
https://github.com/haiyuwu/facebrightness
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/hakeematyab/Neural-Image-Caption-Generator-Show-and-Tell-Paper-Replication
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.