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

https://github.com/Akash-Ramjyothi/Cloud-and-Fog-removal-from-Satellite-Images-using-Generative-Adversarial-Netrowks-GANs-

Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation

https://github.com/AliRoyat/NTIRE2021-IQA-MACS-Pytorch

(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment

https://github.com/Biagig/Learning-Sampling-Pattern

Learning the Sampling Pattern for MRI

https://github.com/DengPingFan/Scoot

Scoot: A Perceptual Metric for Facial Sketches

https://github.com/EgorRazumilov/TAC_GAN

TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

https://github.com/EndyWon/Deep-Feature-Perturbation

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/FaceOnLive/License-Plate-Recognition-SDK-Android

Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

https://github.com/FireFYF/SlimCAE

End-to-end Optimized Image Compression

https://github.com/FireFYF/modulatedautoencoder

End-to-end Optimized Image Compression

https://github.com/GuoQuanhao/PRNet-Paddle

Progressive Image Deraining Networks: A Better and Simpler Baseline

https://github.com/Iamanorange/DSSLIC

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

https://github.com/Image-X-Institute/lpips_torch2tf

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/Jiaming-Liu/BayerUnifyAug

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

https://github.com/KishanKancharagunta/CDGAN

CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation

https://github.com/Kuga23/DL-fastMRI

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

https://github.com/Leozhibin/PReNet-master

Progressive Image Deraining Networks: A Better and Simpler Baseline

https://github.com/LiuLei95/PyTorch-Learned-Image-Compression-with-GMM-and-Attention

Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules

https://github.com/Meghraj-Webllisto/stylegan

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/MindCode-4/code-10/tree/main/CSD

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

https://github.com/MindSpore-paper-code-3/code6/tree/main/csd

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

https://github.com/MindSpore-scientific-2/code-10/tree/main/NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

https://github.com/MindSpore-scientific-2/code-4/tree/main/NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

https://github.com/MindSpore-scientific-2/code-5/tree/main/NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

https://github.com/MindSpore-scientific-2/code-8/tree/main/NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

https://github.com/MindSpore-scientific/code-13/tree/main/NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/Nikolai10/Checkerboard-Context-Model

Joint Autoregressive and Hierarchical Priors for Learned Image Compression

https://github.com/PatrickTUM/SEN12MS-CR-TS

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks

https://github.com/Puzer/stylegan-encoder

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Deep Learning-based Compression Detection for explainable Face Image Quality Assessment

https://github.com/RudreshVeerkhare/StyleGan

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/S-aiueo32/lpips-pytorch

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/SUPERSHOPxyz/stylegan3-gradient

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/SaipingZhang/DVC_P

DVC: An End-to-end Deep Video Compression Framework

https://github.com/VSAnimator/stgan

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks

https://github.com/XiSHEN0220/RANSAC-Flow

RANSAC-Flow: generic two-stage image alignment

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