https://github.com/Feynman1999/MgeEditing
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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/FilLTP89/SeismoALICE
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/FiveEyes/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/FloList/21cmGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/FrancescoMarchesini/pixToPix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/FrancescoSaverioZuppichini/ResNet
https://github.com/Francis-Rings/MotionEditor
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/Francis-Rings/MotionFollower
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/Franklin-Zhang0/ReasonGen-R1
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL
https://github.com/FraunhoferChalmersCentre/mcmc_corr_score_diffusion
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
https://github.com/FriedRonaldo/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Fujiki-Nakamura/ADDA.PyTorch
https://github.com/Funaizhang/dics
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
https://github.com/FutureXiang/edm2
Analyzing and Improving the Training Dynamics of Diffusion Models
https://github.com/G-U-N/Awesome-Consistency-Models
https://github.com/GBATZOLIS/Wespe-Keras
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/GBATZOLIS/conditional_score_diffusion
Conditional Image Generation with Score-Based Diffusion Models
https://github.com/GH3927/CycleGAN-applied-to-cranes
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/GH3927/Pix2Pix-applied-to-cranes
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/GH3927/pix2pix_linemod_duck
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/GH920/Improve-Medical-Image-Classification-with-GAN
https://github.com/GKalliatakis/Keras-VGG16-places365
https://github.com/GXU-GMU-MICCAI/PGAN-Paddle
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/GabrieleMonte/CoMBInE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Garfield35/Doodle
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/GarrickZ2/Image-Denoising
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
https://github.com/GeorgeLekala/ML
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
https://github.com/Gie-ok-Hie-ut/pytorch-stylegan2-distillation
https://github.com/Gintasp/xgan
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/GiovanniPasq/DA-Faster-RCNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/GiuliaLanzillotta/exercises
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Glp91/fire-detection
https://github.com/Godofnothing/pi-GAN
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
https://github.com/Godot-yry/VGG16_mindspore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gojay001/toolkit-DeepLearning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gothos/BK-SDM
BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.