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/Baukebrenninkmeijer/HackGAN
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/BeanGreen247/Python-AI-Arts
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BearNinja123/StyleGAN_ADAnough
https://github.com/BedirYilmaz/picturate-mwml
https://github.com/Belval/TextRecognitionDataGenerator
https://github.com/BenjiKCF/Superesolution-and-watermark-removal
https://github.com/Beny-Maleki/Pix2PixTorch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Beoconcuame/GRNN-Image-Inpainting
Radial Based Analysis of GRNN in Non-Textured Image Inpainting
https://github.com/BeyondCloud/Comp04_ReverseImageCaption
https://github.com/BiancaMMoreno/Artgan-Implementation
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/Bingwen-Hu/DRIT
https://github.com/Blade6570/Learningimage-to-imagetranslationusingpairedandunpairedtrainingsamples
Learning image-to-image translation using paired and unpaired training samples
https://github.com/BoFeng2477/CNNDetection-TF2.0
CNN-generated images are surprisingly easy to spot... for now
https://github.com/BorealisAI/continuous-time-flow-process
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/BrookInternSOMA/pix2pix-barcode
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Bujiazi/HiFlow
HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
https://github.com/Bunnyyyyy/EdgeConnect-Inpainting
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/Burf/ResNet-Tensorflow2
https://github.com/Byte7/GANs
https://github.com/ByteDance-Seed/Bagel
https://github.com/CIA-Oceanix/4dvarnet-core
https://github.com/CIA-Oceanix/DinAE
https://github.com/CIA-Oceanix/DinAE_4DVarNN_torch
https://github.com/CNC-IISER-BHOPAL/Any-Body-Can-Dance
https://github.com/COMP6248-Reproducability-Challenge/Transfer-Learning
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
https://github.com/CREVIOS/MediSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/CS2470FinalProject/X-GAN
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/CS269-Capstone/t-snake-capstone
https://github.com/CS269-Capstone/t-snake-mask-generation
https://github.com/CSDN-AI7/image_caption
https://github.com/CV-Reimplementation/FUNIT-Reimplementation
https://github.com/CV-Reimplementation/ResViT-Reimplementation
ResViT: Residual vision transformers for multi-modal medical image synthesis
https://github.com/CV-Reimplementation/pGAN-Reimplementation
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.