Papers with Code 아카이브

Image Reconstruction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

5

평가표

8

보존된 평가 기록

35

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/PuchatekwSzortach/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/RAGHAV2998/Everybody-Can-Dance-Now-Video-game-version-

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/RomanovIgnat/KernelGAN

Blind Super-Resolution Kernel Estimation using an Internal-GAN

https://github.com/Rust401/pixel2piexl

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/RyotaImai/-Keras-Image2StyleGAN-

Image2StyleGAN++: How to Edit the Embedded Images?

https://github.com/STASYA00/UrbanGen

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/SeonbeomKim/TensorFlow-pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/SharhadBashar/Depth-Percetion

Unsupervised Monocular Depth Estimation with Left-Right Consistency

https://github.com/SnailDev/github-hot-hub

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/Stanrenko/mrf_moco_media_v2

Upper-body free-breathing Magnetic Resonance Fingerprinting applied to the quantification of water T1 and fat fraction

https://github.com/Subhadip-1/adversarial_primal_dual_tomography

Adversarially learned iterative reconstruction for imaging inverse problems

https://github.com/Sulam-Group/LPCNN

Learned Proximal Networks for Quantitative Susceptibility Mapping

https://github.com/TWJianNuo/panoptic-scene-understanding

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/TanviKulkarni07/CS-MRI-Recon

Accelerated MRI with Un-trained Neural Networks

https://github.com/TanyaChutani/Monodepth-Tf2.x

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/TanyaChutani/Pix2pix-Tf2.x

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/TencentARC/FAIG

Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution

https://github.com/ThomasRobertFr/deep-learning-figures

DualDis: Dual-Branch Disentangling with Adversarial Learning

https://github.com/VLOGroup/tdv

Total Deep Variation for Linear Inverse Problems

https://github.com/VedantDere0104/Pix2Pix_GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/Vious/LBAM_Pytorch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/Vladislav-IS/dls_project_pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/WeizmannVision/ssfmri2im

From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI

https://github.com/XJhaoren/Academic-Group

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/XPixelGroup/BasicSR

SwinIR: Image Restoration Using Swin Transformer

https://github.com/XXXVincent/MonoDepth2

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/XavierJiezou/cloud-removal-deploy

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/YCJGG/Muti-style-transfer-by-camera

Universal Style Transfer via Feature Transforms

https://github.com/Yc174/monodepth

Unsupervised Monocular Depth Estimation with Left-Right Consistency

https://github.com/Yodai1996/CV

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/YoungWoong-Cho/Decay

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/YvanG/VQGAN-CLIP

Taming Transformers for High-Resolution Image Synthesis

https://github.com/Zakobian/CT_framework_

Learned Primal-dual Reconstruction

https://github.com/Zdafeng/SwinFIR

SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution

https://github.com/ZezhouCheng/GP-DIP

A Bayesian Perspective on the Deep Image Prior

https://github.com/a-martyn/unet

Image-to-Image Translation with Conditional Adversarial Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.