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/julianjuaner/rival
Real-World Image Variation by Aligning Diffusion Inversion Chain
https://github.com/julik43/IIMAS-USCS
https://github.com/junaidwahid/Implementation-of-StyleGan_A-Style-Based-Generator-Architecture-for-Generative-Adversarial-Networks
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/jung-jun-uk/mixface
https://github.com/junhsss/consistency-models
https://github.com/junnyu/paddle_reformer
https://github.com/justinbellucci/cnn-visualizations-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/justinjohn0306/EverybodyDanceNow-Colab
https://github.com/justinpinkney/toonify
Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains
https://github.com/justusschock/delira_cycle_gan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jvanvugt/pytorch-domain-adaptation
https://github.com/jvoynow/ConvNet-Architectures
https://github.com/jwliao1209/diffqrcode
https://github.com/jwmao1/story-adapter
Story-Adapter: A Training-free Iterative Framework for Long Story Visualization
https://github.com/jxhuang0508/GenCo
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
https://github.com/jy0205/Pyramid-Flow
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/jychoi118/p2-weighting
https://github.com/jyotirmaypaliwal/DCGAN-on-Celeba-dataset
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jzbontar/pixelcnn-pytorch
https://github.com/k--chow/Generative-Well-intentioned-Networks
https://github.com/k-miran/hear
https://github.com/kaiiwoo/infogan-pytorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/kaist-cvml-lab/DreamCatalyst
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
https://github.com/kaist-cvml/dreamcatalyst
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
https://github.com/kaist-dmlab/SELFIE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kakaobrain/coyo-dataset
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/kakaoenterprise/kakaoenterprise.github.io
A Plug-in Method for Representation Factorization in Connectionist Models
https://github.com/kamanphoebe/motiondetection
https://github.com/kamenbliznashki/pixel_models
https://github.com/kampta/multiview-shapes
https://github.com/kangyeolk/pytorch-gan-collections
https://github.com/kanhereashwin/cs236-project
https://github.com/kaonashi-tyc/zi2zi
https://github.com/karfly/qd-imd
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/karl-hajjar/Generative-Adversarial-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.