https://github.com/LiyaoJiang1998/PixelMan
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/LoganOneal/neuralpaint-server
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/LorisDonovan/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Lornatang/CoGAN-PyTorch
https://github.com/Lornatang/DCGAN-PyTorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Lornatang/PyTorch-CycleGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Lornatang/PyTorch-NCSN
Generative Modeling by Estimating Gradients of the Data Distribution
https://github.com/Lornatang/ResNet-PyTorch
https://github.com/Lornatang/WassersteinGAN_DIV-PyTorch
https://github.com/Lornatang/tensorflow-vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Lotayou/CoCosNet
Cross-domain Correspondence Learning for Exemplar-based Image Translation
https://github.com/Lotayou/RATE
Region-adaptive Texture Enhancement for Detailed Person Image Synthesis
https://github.com/Lotayou/everybody_dance_now_pytorch
https://github.com/LouisRouss/Diffusion-Based-Model-for-Colorization
https://github.com/LsAntonio/60daysofudacity
https://github.com/LucasSheng/avatar-net
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
https://github.com/Luccadoremi/Model-Compression-DAQ
Training with Quantization Noise for Extreme Model Compression
https://github.com/LuigiRussoDev/Covid19Detection
https://github.com/LuigiRussoDev/ResNets
https://github.com/LukasMosser/geogan
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
https://github.com/LynnHo/AttGAN-Tensorflow
https://github.com/LynnHo/CycleGAN-Tensorflow-2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/LynnHo/CycleGAN-Tensorflow-PyTorch-Simple
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/LynnHo/DCGAN-LSGAN-WGAN-WGAN-GP-Tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MASILab/SynSeg-Net
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
https://github.com/MCG-NJU/DDT
https://github.com/MECLabTUDA/M3D-NCA
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
https://github.com/MICV-yonsei/DragText
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
https://github.com/MMadhatter/wgan-mindspore
https://github.com/MNaplesDevelopment/DCGAN-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MNaplesDevelopment/Deep-Convolutional-Generative-Adversarial-Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MNaplesDevelopment/MNIST-DCGAN-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MNaseerSubhani/LSE
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation
https://github.com/MQSchleich/SatelliteFAMOS
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet
https://github.com/MS-Mind/MS-Code-04
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.