https://github.com/MS-Mind/MS-Code-06/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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/MS-Mind/MS-Code-06/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MS-P3/code4/tree/main/D2Co
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
https://github.com/MS-P3/code4/tree/main/Diff-MSR
https://github.com/MStypulkowski/mlinpl-generative-workshop
https://github.com/Ma-Dan/EdgeConnect-CoreML
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/MadryLab/robustness_applications
https://github.com/MakeDirtyCode/DC-GAN-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MakeDirtyCode/DCGAN-celebA-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MakeDirtyCode/cDCGAN-celebA-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MaliParag/ScanSSD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Maluuba/GeNeVA
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
https://github.com/Maluuba/GeNeVA_datasets
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
https://github.com/Maouriyan/inpainting_demo
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/MarcCote/NADE
https://github.com/MarkHershey/arxiv-dl
https://github.com/MarvinLavechin/imagetranslation-tensorflow
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/MarvinMartin24/MADA-PL
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/Masao-Taketani/StarGAN-tf2
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/MasayaGit/AnimeGAN
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
https://github.com/MasoumehVahedi/GANs-Model
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MasoumehVahedi/Pix2Pix-GAN-model
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Masquerade51256/DCGAN_SignPicture
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MathiasGruber/PConv-Keras
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/MathiasStensrud/autoBleep
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MathieuRita/SinGAN_styletransfer
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Maveric4/pr19aaw01
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MaxHolmberg96/WaveGAN
https://github.com/Maxencephilbert/In_Silico_Labeling_CBIO_internship
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Adversarially_Learned_Inference
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Image_Prior
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Denoising_Diffusion_Implicit_Models
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Denoising_Diffusion_Probabilistic_Models
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Flow_Straight_and_Fast_Learning_to_Generate_and_Transfer_Data_with_Rectified_Flow
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Image_to_Image_Translation_with_Conditional_Adversarial_Nets
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.