https://github.com/Joostdambrink/GeniusAI
Enhanced Deep Residual Networks for Single Image Super-Resolution
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/Josien94/MLiP
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/JunnYu/x-transformers-paddle
CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/KPIxLILU/Machine-Learning-Workshop
https://github.com/Karan-kapadia/Style-Transfer-karan
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Kavya9294/MLFinalProject
https://github.com/KeremTurgutlu/papers
https://github.com/KhanJr/Generative-Adversarial-Networks-COMPUTER-VISION
https://github.com/Kinyugo/consistency_models
https://github.com/Kumara-Kaushik/proba_v_challenge
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/KunStats/Paddle-DIP
https://github.com/LCM1999/VolumeRescaling
https://github.com/LEEPEIQIN/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/LONG-9621/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/LWChen20/RCAN
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/LYL1015/ResDiff
ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution
https://github.com/LabForComputationalVision/bias_free_denoising
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/Labyz/SinGAN-MVA
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/LakshmiTeja17/NNFL-Project
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/LeonSun0101/CD-SD
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/Leozhibin/PReNet-master
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/LongguangWang/ArbSR
Learning A Single Network for Scale-Arbitrary Super-Resolution
https://github.com/LongguangWang/Scale-Arbitrary-SR
Learning A Single Network for Scale-Arbitrary Super-Resolution
https://github.com/LorisDonovan/GAN
https://github.com/Lornatang/CSNLN-PyTorch
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/Lornatang/DRRN-PyTorch
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/Lornatang/ESPCN-PyTorch
https://github.com/Lornatang/ESRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Lornatang/FSRCNN-PyTorch
https://github.com/Lornatang/KernelGAN
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/Lornatang/Meta_RDN-PyTorch
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
https://github.com/Lornatang/RFB_ESRGAN-PyTorch
Perceptual Extreme Super Resolution Network with Receptive Field Block
https://github.com/Lornatang/SFTMD-PyTorch
https://github.com/Lornatang/SRCNN-PyTorch
https://github.com/Lornatang/SRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Lornatang/SRGAN_PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.