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/Spoorthy-gunda/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SrinjaySarkar/NICE
https://github.com/SrinjaySarkar/REAL-NVP
https://github.com/Sshanu/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/Stevan-Zhuang/image-domain-transfer
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/SteveKommrusch/LuNG3D
Synthetic Lung Nodule 3D Image Generation Using Autoencoders
https://github.com/Sudhir11292rt/myBDL
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/SummerHuiZhang/StarGAN_Norland
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/SummerHuiZhang/StarGAN_test
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/SunnerLi/P-Conv
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/SupratimH/neural-style-transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SusungHong/IF-DreamFusion
https://github.com/SuyashSonawane/fakedetector
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/SzuPc/MindSpore-StyleGAN2
https://github.com/T-C-J/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/T-C-J/shuiying
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/TAMU-VITA/AGD
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
https://github.com/TFNTF/PostEdit
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
https://github.com/TJJTJJTJJ/pytorch_CycleGAN_frame
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/TUM-ML-Lab18/FaceSwap
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/TahmasbiM/Example
https://github.com/Takuya-Shuto-engineer/PokemonGAN
https://github.com/TalFurman/Implict_neural_representation_of_images
Implicit Neural Representations with Periodic Activation Functions
https://github.com/Talgin/facerec
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/TanviKulkarni07/CS-MRI-Recon
https://github.com/TanyaChutani/Pix2pix-Tf2.x
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/TanyaChutani/WGAN-TF2
https://github.com/TanyaChutani/WGAN-TF2.x
https://github.com/Taoudi/LotteryTicketHypothesis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Taoudi/Monet-ization-of-Images
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Tejas-Nanaware/GAN-Anime-Characters
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/TencentYoutuResearch/Adv-Makeup
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
https://github.com/TerminatorSd/generative
https://github.com/ThanasisMattas/smartflow
https://github.com/TheDenk/InstantID-Visual-Prompt
InstantID: Zero-shot Identity-Preserving Generation in Seconds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.