Papers with Code 아카이브

3D Face Animation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

3

보존된 평가 기록

12

코드

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

https://github.com/laomao0/BIN

Video Enhancement with Task-Oriented Flow

https://github.com/laowng/GISR

Deep Back-Projection Networks For Super-Resolution

https://github.com/lee-aaron/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/leftthomas/espcn

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/linxi159/GAN-training-tricks

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/linxi159/Tips-and-tricks-to-train-GANs

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/liuzhen03/ADNet

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/lizhuoq/ESRGAN-pytorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/lucasmfaria/Machine-Learning-Capstone

XGBoost: A Scalable Tree Boosting System

https://github.com/luciennnnnnn/dualformer

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/markmaxt/VideoSR

Deep Back-Projection Networks For Super-Resolution

https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/med-seg/kidney

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/med-seg/kidney-mc

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/mediatek-neuropilot/mai22-real-time-video-sr

Revisiting Temporal Modeling for Video Super-resolution

https://github.com/michael13162/DoodleGAN

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/mindspore-ai/models/tree/master/research/cv/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/mtorabirad/PricePrediction

XGBoost: A Scalable Tree Boosting System

https://github.com/mukul1093/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/murrman95/INF573Project2018

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/mv-lab/swin2sr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/nabarunbaruaAIML/ATCC_Yolov5

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/nagadomi/waifu2x

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/nannau/DoWnGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/niazwazir/REAL_TIME_VIDEO_IMSR

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/nihui/realsr-ncnn-vulkan

https://github.com/npielawski/pytorch_tiramisu

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/nsubbaian/FrequentistML

XGBoost: A Scalable Tree Boosting System

https://github.com/olaviinha/NeuralImageSuperResolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/omglu93/energy_consumption_eda_prediction

XGBoost: A Scalable Tree Boosting System

https://github.com/omkarghugarkar007/Neural_Super_Sampling

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/README.md

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

https://github.com/open-mmlab/mmsr

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/osofr/xgboost

XGBoost: A Scalable Tree Boosting System

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