https://github.com/AnjanaGJoseph/Super-Resolution-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Papers with Code 아카이브
3D Object Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
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/Anou9531/Bayesian-CNN
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/Ansheel9/GANs
https://github.com/AntonioAlgaida/Edge.SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/AntreasAntoniou/DAGAN
https://github.com/Apogentus/style-transfer-with-strength-control
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Aqsa-K/SRGAN-Keras
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Araxeus/PNG-Upscale
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/ArchieMeng/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/Ars235/Novelty_Detection
https://github.com/Ash-Lee233/IRN
https://github.com/AtharvBhat/Plus-Ultra
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AureliePeng/Keras-WaveletTransform
https://github.com/Automunge/AutoMunge
https://github.com/Avinandan22/Blog-on-GANs-for-PClub
https://github.com/BAMresearch/Photothermal_SR_Net
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
https://github.com/BIOINSu/LGFN
https://github.com/BYOGan/BYOGan
https://github.com/BasileBron/Artificial-Intelligence-Lexical
https://github.com/BearNinja123/esrGAN_vBearNinja123
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/BilcSergiu/DAGAN
https://github.com/BillOuyang/KernelGan-s3
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/BoFeng2477/CNNDetection-TF2.0
CNN-generated images are surprisingly easy to spot... for now
https://github.com/Build-Week-Spotify-Song-Suggester-5/Data-Science
https://github.com/Byte7/GANs
https://github.com/CREVIOS/MediSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/CYetlanezi/Proyecto-Opti
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Capstone-Projects-2019-Fall/GAN_Image_Editor
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Chad4545/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChengBinJin/Real-time-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChenyangLEI/deep-video-prior
Neural Radiance Flow for 4D View Synthesis and Video Processing
https://github.com/ChristophReich1996/Mode_Collapse
https://github.com/Chubbyman2/GAN_Waifu_Generator
https://github.com/Coldog2333/pytoflow
https://github.com/CorentinMAG/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/CosmoStat/rca
Constraint matrix factorization for space variant PSFs field restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.