Papers with Code 아카이브
Image Generation
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- 평가표
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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/ctwxdd/Tensorflow-ACGAN-Anime-Generation
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
https://github.com/cuiaiyu/street-tryon-benchmark
Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images
https://github.com/cure-lab/pnpinversion
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
https://github.com/curioserve/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/cuteyyt/ImageSteganography
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
https://github.com/cvfx-2019/homework1-color-transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cvfx-2019/homework3-GAN-Dissection
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
https://github.com/cvlab-columbia/zero123
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors
https://github.com/cvp19g2/cvp19g2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cvqluu/Additive-Margin-Softmax-Loss-Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/cvqluu/Angular-Penalty-Softmax-Losses-Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/cwolfbrandt/csk
https://github.com/cwvisuals/FleshDigressions
https://github.com/cwvisuals/StyleGAN2
https://github.com/cydessole/AnimeLife
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cyjdlhy/DreamInit
A General Framework to Boost 3D GS Initialization for Text-to-3D Generation by Lexical Richness
https://github.com/cyprienruffino/pixelwise
https://github.com/cyrilzakka/GANLocalEditing
https://github.com/cyrilzakka/stylegan2-tpu
https://github.com/czm0/draw_pytorch
https://github.com/d2l-ai/d2l-en
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
https://github.com/d2l-ai/d2l-tr
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
https://github.com/dabsdamoon/Anime-Colorization
https://github.com/dabsdamoon/Anime-Colorization-v0.2
https://github.com/dacostaHugo/AC-GANs
https://github.com/dagrate/gan_network
https://github.com/daheyinyin/wgan
https://github.com/daheyinyin/wgan_gp
https://github.com/daimonae/Fundus-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/daintlab/ct-denoising
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/daixiangzi/ImprovedGan-pytorch
https://github.com/dakenan1/Everybody-Dance-Now
https://github.com/dalmia/siren
Implicit Neural Representations with Periodic Activation Functions
https://github.com/damo-vilab/videocomposer
Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/dan8991/Image-coding-perceptual-enhancement-with-diffusion-models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.