Papers with Code 아카이브

3D Object Super-Resolution

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3D object super-resolution is the task of up-sampling 3D objects.

<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>

코드

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

https://github.com/SaubanMusaddiq/Conditional-Gans

Generative Adversarial Networks

https://github.com/SaubanMusaddiq/Conditional-Image-Synthesis-using-DCGANS

Generative Adversarial Networks

https://github.com/SaubanMusaddiq/GAN-playground

Generative Adversarial Networks

https://github.com/Scallions/ClassSR_paddle

ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

https://github.com/SeungyounShin/CAGFace

Component Attention Guided Face Super-Resolution Network: CAGFace

https://github.com/ShakedDovrat/JpegArtifactRemoval

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

https://github.com/Shakib-IO/Diminishing_Image_Noise_Using_Deep_Learning

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

https://github.com/ShambhawiVarchasva/SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/Shito0907/cat_gen

Generative Adversarial Networks

https://github.com/Shritesh99/100DaysofMLCodeChallenge

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Shritesh99/Image_Super_Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN

Generative Adversarial Networks: An Overview

https://github.com/SimoneDutto/EDSR

Enhanced Deep Residual Networks for Single Image Super-Resolution

https://github.com/Slayerxxx/E-RAISR

Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning

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

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/SofiaBlinova/EDSR-net

Enhanced Deep Residual Networks for Single Image Super-Resolution

https://github.com/SongDark/generate_normal

Generative Adversarial Networks

https://github.com/Suanmd/TR-MISR

https://github.com/SuchismitaSahu1993/Studying-the-Robustness-of-GANs-on-Noisy-Data-using-MNIST-and-EMNIST-Datasets

Generative Adversarial Networks

https://github.com/TAMU-VITA/GAN-Slimming

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

https://github.com/THINKWARE-AI/NCAP

NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior

https://github.com/TahmasbiM/Example

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/TanguyJeanneau/white-mirror

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/TanyaChutani/Image-Super-Resolution-SRGAN-TF2.0

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/Tee0125/pytorch-fast-style-transfer

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/TencentARC/FAIG

Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution

https://github.com/TingdiRen/URSCT-SESR

Underwater Image Super-Resolution using Deep Residual Multipliers

https://github.com/TomokiKomiya/SRGAN-keras

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/TottiPuc/Hyperspectral_ImageReconstruction_GAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/TrizteX/Audio-SuperRes

Audio Super Resolution using Neural Networks

https://github.com/TuSimple/RangeDet

RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection

https://github.com/UBERCRUZER/superresolution

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/UdbhavPrasad072300/GANs-Implementations

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/UdbhavPrasad072300/Image-Super-Resolution

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/V0LsTeR/mySRResNet

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/V0LsTeR/mySuperRes

Enhanced Deep Residual Networks for Single Image Super-Resolution

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