Papers with Code 아카이브
Image Manipulation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 2
Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LonglongaaaGo/EXE-GAN
Paint by Example: Exemplar-based Image Editing with Diffusion Models
https://github.com/MICV-yonsei/DragText
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
https://github.com/MS-P3/code4/tree/main/D2Co
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
https://github.com/MathieuRita/SinGAN_styletransfer
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Mind23-2/MindCode-78
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Mind23-2/MindCode-88/tree/main/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MindSpore-scientific-2/code-10/tree/main/SING
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MindSpore-scientific/code-8/tree/main/SING
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/PKU-YuanGroup/UniWorld-V1
UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
https://github.com/PaddlePaddle/PaddleNLP
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/PaulEmmanuelSotir/DeepCV
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Player514/BachelorThesis
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ProGamerGov/neural-style-pt
https://github.com/PruneTruong/GLU-Net
Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations
https://github.com/ShenYujun/InterFaceGAN
Interpreting the Latent Space of GANs for Semantic Face Editing
https://github.com/ThomasRobertFr/deep-learning-figures
DualDis: Dual-Branch Disentangling with Adversarial Learning
https://github.com/VEDANTGHODKE/FFHQ-Ageing-Dataset
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/Wenyuan-Vincent-Li/sinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Zhangyanbo/iResNetLab
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/abdallahyamie/python-master
https://github.com/adam-mehdi/MuarAugment
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/adriaciurana/DragGAN
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
https://github.com/adriaciurana/exploration-stylegan-nada
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
https://github.com/aimiokab/misr-s2
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
https://github.com/akashgit/stylegan2_wt
https://github.com/akatigre/multi2one
https://github.com/alibaba/graph-gpt
https://github.com/allenai/ai2thor-rearrangement
https://github.com/andreas128/NTIRE21_Learning_SR_Space
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/aniruddha343/Two-stream-Encoder-Decoder-Network-for-Image-Forensics
Two-stream Encoder-Decoder Network for Localizing Image Forgeries
https://github.com/ariel415el/Efficient-GPNN
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models
https://github.com/ariel415el/Simple-SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/arraiyopensource/torchgeometry
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/basilevh/image-outpainting
SinGAN: Learning a Generative Model from a Single Natural Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.