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/PaddlePaddle/PaddleNLP
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/pipelines/examples/text_to_image
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers/examples/dreambooth
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/PaddlePaddle/PaddleScience
Implicit Neural Representations with Periodic Activation Functions
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/Pal-kid404/F-ANcGAN
https://github.com/Panagiotou/Random3DTerrain
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/PathPlanning/GAN-Path-Finder
https://github.com/PatrickTUM/UnCRtainTS
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
https://github.com/Paul-Trax/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/PaulEmmanuelSotir/DeepCV
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Pavan-pk/ArtGan_Realism
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Pengxiao-Wang/Typeface-and-Font-Style-Transfer
https://github.com/PeteXC/Transfer-Learning
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
https://github.com/Petecheco/CLR-GAN
https://github.com/PeterZhizhin/HSE-DeepLearning-NCSN
Generative Modeling by Estimating Gradients of the Data Distribution
https://github.com/PieterBijl/Group28
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Pillercottrer/radcap_project
https://github.com/Pingxia/ConvolutionalSketchInversion
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
https://github.com/Player514/BachelorThesis
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Pointcept/GPT4Point
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PracNeyman/attention_guided_img_trans
https://github.com/Prakarsh-Bhardwaj/auto-animeface-generation-using-DCGAN-pytorch
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
https://github.com/PramuPerera/In2I
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
https://github.com/Pranav-Patil-7/Automatic_Image_Captioning_Project
https://github.com/PrasannaPulakurthi/MMD-AdversarialNAS
https://github.com/PrasannaPulakurthi/MMD-PMish-NAS
https://github.com/PrasannaPulakurthi/ProGAN_PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/PrashanthaTP/wav2mov
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/Prinsphield/GeneGAN
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
https://github.com/ProGamerGov/neural-style-pt
https://github.com/ProSTIGTeam/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Proxim123/person-reID-No1-
Joint Discriminative and Generative Learning for Person Re-identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.