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

https://github.com/rahuls02/Image-Noise-Reduction

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

https://github.com/railgun202/stylized-neural-painting

Stylized Neural Painting

https://github.com/raj-gupta1/Flower-Species-Classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/raj-gupta1/Tomato-leaves-disease-Predictor

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/rajatsahay/Pose2Pose

Everybody Dance Now

https://github.com/rajneeshaggarwal/google-efficientnet

Deep Residual Learning for Image Recognition

https://github.com/random-quark/pix2pix-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ranjith0430/ML-tutorials

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/raoofzare/Pix2Pix_cityscapes

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ravindrabharathi/FaceAgingGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/realmichaelye/Skin-Lesions-Classifier-ResNet50

Deep Residual Learning for Image Recognition

https://github.com/researchmm/SiamDW

Deep Residual Learning for Image Recognition

https://github.com/rhero12/Slic

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/richardobi/medigan

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/richieyoum/Pneumonia-diagnosis

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/rickyHong/AnnieGan-repl

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/rickyHong/pix2pixHD-repl

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/rickyHong/tfRecord-Caltech256-repl2

Deep Residual Learning for Image Recognition

https://github.com/rickyhan/SimGAN-Captcha

Learning from Simulated and Unsupervised Images through Adversarial Training

https://github.com/rinnakk/japanese-stable-diffusion

Pretraining is All You Need for Image-to-Image Translation

https://github.com/rish-av/gan_spectral_matching

Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize

https://github.com/rishabh-vij/Neural_Style_transfer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/rishabmallick/Sign-language-live-predictor

Deep Residual Learning for Image Recognition

https://github.com/rizqiamaliatuss/PotatoLeafDiseaseClassification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/robertofranceschi/Image-classification-on-Caltech101-using-CNNs

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/robin-ex/TokenFusion

Multimodal Token Fusion for Vision Transformers

https://github.com/robot-learning-freiburg/DynaFill

Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning

https://github.com/rohitkuk/Cartoonify

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ronny3050/AdvFaces

WarpGAN: Automatic Caricature Generation

https://github.com/roshankoirala/Fire_Detection_Model

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/rpryzant/proxy-a-distance

Domain-Adversarial Training of Neural Networks

https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

https://github.com/rtanno21609/AdaptiveNeuralTrees

Deep Residual Learning for Image Recognition

https://github.com/ruanyuhui/adn-qsdl

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

https://github.com/ruiliu-ai/stereogan

StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching

https://github.com/ruinmessi/RFBNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

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