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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AuroraCoboAguilera/RobustSampling
공식 코드https://github.com/Azure/mmlspark
공식 코드https://github.com/BNL-DAQ-LDRD/NeuralCompression_v3
공식 코드https://github.com/BUPTLdy/RICE_DATASET
공식 코드https://github.com/Bala93/Context-aware-segmentation
공식 코드A context based deep learning approach for unbalanced medical image segmentation
https://github.com/BaratiLab/FluidGAN-TensorFlow
공식 코드Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
https://github.com/Basvanstein/doe2vec
공식 코드DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
https://github.com/BeautyOfWeb/AffinityNet
공식 코드AffinityNet: semi-supervised few-shot learning for disease type prediction
https://github.com/Benjamin-Lee/deep-rules
공식 코드https://github.com/BethanyL/DeepKoopman
공식 코드Deep learning for universal linear embeddings of nonlinear dynamics
https://github.com/BiDAlab/DeepSignDB
공식 코드https://github.com/BiDAlab/DiveFace
공식 코드Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics
https://github.com/BiDAlab/TypeNet
공식 코드https://github.com/BingyaoHuang/Neural-STE
공식 코드Modeling Deep Learning Based Privacy Attacks on Physical Mail
https://github.com/BjoernNieth/LS-Dataset-pruning-in-AT
공식 코드Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
https://github.com/Bluefog-Lib/bluefog
공식 코드BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
https://github.com/BorealisAI/dcf
공식 코드https://github.com/BorgwardtLab/mgp-tcn
공식 코드https://github.com/Brain-Bridge-Lab/resmem
공식 코드Embracing New Techniques in Deep Learning for Estimating Image Memorability
https://github.com/BrightBlueCheese/Modeling-of-Time-varying-Wireless-Communication-Channel-with-Fading-and-Shadowing
공식 코드Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing
https://github.com/C4ntor/GeoHAR-SPDNet
공식 코드Geometric Deep Learning for Realized Covariance Matrix Forecasting
https://github.com/CAMMA-public/ai4surgery
공식 코드Artificial Intelligence in Surgery: Neural Networks and Deep Learning
https://github.com/CDECatapult/ml-performance-prediction
공식 코드https://github.com/CDInstitute/Building-Dataset-Generator
공식 코드Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture
https://github.com/CGCL-codes/VulDeePecker
공식 코드VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
https://github.com/CIFASIS/wganvo
공식 코드WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks
https://github.com/CSL-KU/TinyLidarNet
공식 코드TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing
https://github.com/Cadene/torchnet-deep6
공식 코드https://github.com/CagkanYapar/LocUNet
공식 코드LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning
https://github.com/CedArctic/rnnoise-ex
공식 코드RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features
https://github.com/Cerenaut/bilateral-brain
공식 코드Deep learning in a bilateral brain with hemispheric specialization
https://github.com/ChristianMarzahl/EIPH_WSI
공식 코드Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
https://github.com/ChristianMarzahl/Exact
공식 코드https://github.com/ChuanqiChenCC/CGKN
공식 코드https://github.com/ChunpingQiu/benchmark-on-So2SatLCZ42-dataset-a-simple-tour
공식 코드Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.