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/chayanchatterjee/cbc-skynet
https://github.com/chayanchatterjee/gw-skylocator
https://github.com/chefpr7/Image-Inpainting-using-Partial-Convolutional-Layers
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/chen-hao-chao/dlsm-toy
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
https://github.com/chendrag/sfbc
https://github.com/chenggongliang/arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/chenguolin/DiffSplat
SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
https://github.com/chenguolin/InstructScene
https://github.com/chenkang121/DANN
https://github.com/chenller/mmseg-extension
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
https://github.com/chenqiguo/gan_replication
https://github.com/chethankodase/alma
https://github.com/chez8990/Image2ImageTranslation
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/chinhsuanwu/dreamfusionacc
https://github.com/chipsi/FUNIT
https://github.com/chiqunz/Unsupervised_Models
https://github.com/chjw1475/localbasis
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
https://github.com/chldydgh4687/2020-1.VideoCaptioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chosj95/PFPNet.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chouhan-avinash/DGMR
https://github.com/chrhenning/hypercl
https://github.com/chrischute/glow
https://github.com/chrischute/real-nvp
https://github.com/christabella/bruno
https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
https://github.com/christopher-beckham/hologan-pytorch
HoloGAN: Unsupervised learning of 3D representations from natural images
https://github.com/christopher-beckham/toy-edm
Elucidating the Design Space of Diffusion-Based Generative Models
https://github.com/christophmeyer/stylegan2-tf2
https://github.com/chrvt/denoising-normalizing-flow
https://github.com/chuanqichen/deepcoaching
https://github.com/chuanyangjin/fast-dit
https://github.com/chunlei2/show-and-tell
https://github.com/cianfrocco-lab/GAN-for-Cryo-EM-image-denoising
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/cihanongun/3D-CGAN
Paired 3D Model Generation with Conditional Generative Adversarial Networks
https://github.com/civilman628/pgan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.