Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/danielamassiceti/CCA-visualdialogue
https://github.com/danielparada1/RL_SpaceInvaders
https://github.com/dantecomedia/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/darkhill11/convolutional_GAN_basics
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/darr/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/darrishabh/coviprox
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/darth-c0d3r/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/dashayushman/TAC-GAN
https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_micro_sol.ipynb
https://github.com/datawiz1984/MLND_Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datawiz1984/malaria-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datduong/stylegan2-ada-Ws-22q
https://github.com/datoboat/Vehicle-Detection
https://github.com/davidemartinelli/PixelCNN
https://github.com/davidhalladay/CE-compressive-sensing-on-generative-model-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/davidismael/BEGAN
https://github.com/davidstap/AttnGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/davidsvy/interactive-evolution
https://github.com/dbash/CycleGAN3D
https://github.com/dbash/visda2022-org
https://github.com/ddcas/p2gan-pytorch
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
https://github.com/dddb11/MVSS-Net
Image Manipulation Detection by Multi-View Multi-Scale Supervision
https://github.com/dead-mazai/avia-missing-airplanes
https://github.com/deep-floyd/if
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
https://github.com/deepak112/Keras-SRGAN
https://github.com/deepakprabakar96/DTN
https://github.com/deepcodebase/inpaint
https://github.com/deepfake-study/guided-diffusion
https://github.com/deepfakes/faceswap
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
https://github.com/deepinx/iqiyi-vid-challenge
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deepmind/dm-haiku
https://github.com/deepmind/graphcast
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
https://github.com/deepware/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.