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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Cogito2012/DEAR
공식 코드https://github.com/ContextScout/ned-graphs
공식 코드https://github.com/CrawlScript/tf_geometric
공식 코드Efficient Graph Deep Learning in TensorFlow with tf_geometric
https://github.com/CupidJay/NRS_pytorch
공식 코드https://github.com/Cybonic/DL_vineyard_segmentation_study
공식 코드Multispectral Vineyard Segmentation: A Deep Learning approach
https://github.com/D-Roberts/lq_backprop
공식 코드https://github.com/D-X-Y/Awesome-AutoDL
공식 코드Automated Deep Learning: Neural Architecture Search Is Not the End
https://github.com/DIVA-DIA/DIVA-DAF
공식 코드DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis
https://github.com/DPBayes/Towards-Efficient-Scalable-Training-DP-DL
공식 코드Towards Efficient and Scalable Training of Differentially Private Deep Learning
https://github.com/DanielMengLiu/DeepLip
공식 코드Exploring Deep Learning for Joint Audio-Visual Lip Biometrics
https://github.com/Daniela-Shereen/GBRT-for-TSF
공식 코드Do We Really Need Deep Learning Models for Time Series Forecasting?
https://github.com/DavidHason/Causal_Analysis
공식 코드https://github.com/DavideBuffelli/TrASenD
공식 코드Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
https://github.com/Debasish-Roy-IISc/PIDL-gradient-plasticity
공식 코드Physics Informed Deep Learning for Strain Gradient Continuum Plasticity
https://github.com/Deep-MI/FastSurfer
공식 코드FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
https://github.com/Deep-Machine-Vision/he-cka-ensembles
공식 코드Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
https://github.com/DeepLabCut/DeepLabCut
공식 코드Markerless tracking of user-defined features with deep learning
https://github.com/DeepLabCut/Primer-MotionCapture
공식 코드A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
https://github.com/DeepMIALab/AI-FFPE
공식 코드https://github.com/DeepPSP/torch_ecg
공식 코드Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing
https://github.com/DeepPathology/TUPAC16_AlternativeLabels
공식 코드https://github.com/DeepRPCA/Denise
공식 코드Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices
https://github.com/DeepRegNet/DeepReg
공식 코드DeepReg: a deep learning toolkit for medical image registration
https://github.com/DengPingFan/PraNet
공식 코드https://github.com/DennisCraandijk/DL-Abstract-Argumentation
공식 코드https://github.com/DexterJZ/eval_driving_safety
공식 코드Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
https://github.com/DiegoKoz/scisci
공식 코드Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
https://github.com/DiffEqML/diffeqml-research/tree/master/dissecting-neural-odes
공식 코드https://github.com/DixonCider/TCRI-Benchmark
공식 코드https://github.com/DreamIP/StrainNet
공식 코드https://github.com/DreamingRaven/Nemesyst
공식 코드https://github.com/DreamingRaven/python-reseal
공식 코드https://github.com/EEmGuzman/resunet-cmb
공식 코드https://github.com/ETVP/medical-image-list
공식 코드A Systematic Collection of Medical Image Datasets for Deep Learning
https://github.com/EUCLID-code/EUCLID-hyperelasticity-NN
공식 코드NN-EUCLID: deep-learning hyperelasticity without stress data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.