Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/olivesgatech/Spatial-Pooling-in-IQA
https://github.com/orange-opensource/cool-chic
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
https://github.com/pbaylies/stylegan-encoder
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/pkorus/l3ic
https://github.com/pminhtam/NBNet
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/pritishuplavikar/All-In-One-Underwater-Image-Enhancement-using-Domain-Adversarial-Learning
All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
https://github.com/qzramiz/End-To-End-Image-Steganography
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/rezafuru/generalized-octave-convolution-compression
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
https://github.com/robo-warrior/SR
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/roimehrez/PIRM2018
https://github.com/ryanxingql/image-quality-assessment-toolbox
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/saadzia10/Steganography-Deep-Learning
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/sharifamit/rfrl-net
https://github.com/simonsLiang/PReNet_paddle
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/singnet/super-resolution-service
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/soumik12345/AODNet
https://github.com/stefkim/stylegan-batik
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/subhayanmukherjee/deeptune
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance
https://github.com/szWingLee/spsr-master
Structure-Preserving Super Resolution with Gradient Guidance
https://github.com/tensorflow/compression
https://github.com/tensorflow/models/tree/master/research/gan
https://github.com/teresasun/docUnet.pytorch
https://github.com/thfylsty/ImageFusion_Dualbranch_Fusion
https://github.com/treammm/Compression
https://github.com/umutsuluhan/HSTRNet-Interp
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
https://github.com/v1t0ry/OTM-AAL
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
https://github.com/valfride/lpsr-lacd
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
https://github.com/vita-group/gnt
https://github.com/vskulkarni31/proSR
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/vskulkarni31/srgan
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/woctezuma/stylegan2-projecting-images
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/wwhio/megmodels
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/xinjie-q/distributed-dvc
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
https://github.com/xinjie-q/ldmic
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
https://github.com/xxh96/ADCSR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.