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/JianqiangRen/AAMS
https://github.com/JimmyDoan1309/BEGAN---Tensorflow-implementation
https://github.com/Jinsung-Jeon/DomainAdaptation
https://github.com/Jinsung-Jeon/Unsupervised-Domain-Adaptation-Through-Self-Supervision
https://github.com/Jireh-Jam/R-MNET-A-Perceptual-Adversarial-Network-for-Image-Inpainting
R-MNet: A Perceptual Adversarial Network for Image Inpainting
https://github.com/JoGo175/diffuse-treevae
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
https://github.com/JobQiu/hackrice
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/JoeTracey/Portfolio
https://github.com/JohanYe/CycleGAN-Pokemon
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JohanZYe/CycleGAN-Pokemon
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JojiJoseph/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/JonasLoos/h-space-directions
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models
https://github.com/JoonyoungYi/CFNADE-keras
https://github.com/Joool/ReproducabilityCNNEasyToSpot
CNN-generated images are surprisingly easy to spot... for now
https://github.com/Jopapy19/CNN-App
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JorisRoels/domain-adaptive-segmentation
https://github.com/JosephineRabbit/Relation-GAN
When Relation Networks meet GANs: Relation GANs with Triplet Loss
https://github.com/JoyPang123/Textmage
https://github.com/JsFlo/Generative-Adversarial-Nets
https://github.com/JuanCasian/ML-Speech-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JuanLuisGonzalez/FAL_net
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
https://github.com/JuliaTomo/DeepImagePrior.jl
https://github.com/JuliaTomo/DeepReconstruction.jl
https://github.com/JuliaWolleb/diffusion-anomaly
https://github.com/JunnYu/x-transformers-paddle
CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/JunsikChoi/Pytorch-InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/JunsikChoi/Pytorch-InfoGAN-CR
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/JusticeLeee/DC_GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Justin-Tan/generative-compression
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/KAIST-Visual-AI-Group/SyncTweedies
https://github.com/KPMG-wiseuniv/AI
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/KU-CVLAB/MIDMs
Cross-domain Correspondence Learning for Exemplar-based Image Translation
https://github.com/KabirSingh114/DeepFake_Face_Detection
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
https://github.com/KaimingHe/deep-residual-networks
https://github.com/KaitavMehta95/Insight-Ganscape-Studio
Progressive Growing of GANs for Improved Quality, Stability, and Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.