https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/francois-rozet/mmps-benchmark
Denoising Diffusion Models for Plug-and-Play Image Restoration
https://github.com/francois-rozet/piqa
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/francois-rozet/zuko
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
https://github.com/franknb/Self-attention-DCGAN
https://github.com/franroldans/tfm-franroldan-wav2pix
https://github.com/fraxea/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/freder-chen/vitp
https://github.com/frederictost/alae_tf2
https://github.com/freedombenLiu/gandissect
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
https://github.com/freegyp/stylegan-keras-ece655
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/freemty/urbangiraffe
UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields
https://github.com/frgfm/torch-cam
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
https://github.com/friedmanroy/ctrl-shift
Elucidating the Design Space of Diffusion-Based Generative Models
https://github.com/friedmanroy/hi-generation
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/fromme0528/pytorch-WaveGAN
https://github.com/ftoralesacosta/gsgm_for_eic_calo
Progressive Distillation for Fast Sampling of Diffusion Models
https://github.com/fuenwang/CVFX-hw1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/fungtion/DANN
https://github.com/funkdub/DMGN
https://github.com/fuqinshen/UGATIT-pytorch
https://github.com/futscdav/Chunkmogrify
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
https://github.com/futurexiang/soda
SODA: Bottleneck Diffusion Models for Representation Learning
https://github.com/g2zac/generative_inpainting
https://github.com/g4vrel/CFM
https://github.com/g4vrel/DDPM
https://github.com/g4vrel/sde_ddpm
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/g4vrel/simple_ml/blob/main/NICE/README.md
https://github.com/g4vrel/simple_ml/tree/main/InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/g4vrel/simple_ml/tree/main/WGAN
https://github.com/g61412333/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/gaborvecsei/SLE-GAN
https://github.com/gabrieletiboni/Domain-adaptation-on-PACS-dataset
https://github.com/gafniguy/4D-Facial-Avatars
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction
https://github.com/gagan16/Cyclegan-tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/gagan16/DcGan-Tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.