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Edge-computing
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Deep Learning on EDGE devices
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AISDC/CookieNetAE
공식 코드Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval
https://github.com/AISDC/DNNTrainerFlow
공식 코드Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval
https://github.com/AmirEstiri/Fed3Att
공식 코드Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks
https://github.com/Azzam-Alhussain/QDCGAN-PYNQ
공식 코드Hardware-Efficient Deconvolution-Based GAN for Edge Computing
https://github.com/BUAA-CI-Lab/GNN-Feature-Decomposition
공식 코드Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
https://github.com/CN-TU/adversarial-recurrent-ids
공식 코드SparseIDS: Learning Packet Sampling with Reinforcement Learning
https://github.com/ChFrenkel/DirectRandomTargetProjection
공식 코드https://github.com/CoderTylor/IoV-SFDL-Swarm-Federated-Deep-Learning-
공식 코드A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles
https://github.com/D0352276/CSL-YOLO
공식 코드CSL-YOLO: A New Lightweight Object Detection System for Edge Computing
https://github.com/DamianoBrunori/MultiUAV-OpenAIGym
공식 코드A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems
https://github.com/EverettRichards/Edge-CAV
공식 코드Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
https://github.com/FraunhoferIVV/fastiot
공식 코드FastIoT -- A framework and holistic approach for rapid development of IIoT systems
https://github.com/HelloZicky/DUET
공식 코드https://github.com/Hoangpham13579/end_to_end_anomaly_detection
공식 코드Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System
https://github.com/IBM/adaptive-federated-learning
공식 코드Adaptive Federated Learning in Resource Constrained Edge Computing Systems
https://github.com/ImanRHT/QECO
공식 코드https://github.com/Inpyo-Hong/AKT-Advanced-knowledge-Transfer
공식 코드https://github.com/JedMills/MTFL-For-Personalised-DNNs
공식 코드Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
https://github.com/JoyZhang20/SRCL_Simu5G
공식 코드Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems
https://github.com/MinodRashmika/APSO_SAC_IIOT
공식 코드Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
https://github.com/PSCLab-ASU/Systolic-CNN
공식 코드https://github.com/PSCLab-ASU/SystolicCNN
공식 코드https://github.com/PeaBrane/eye_track_spatiotemporal
공식 코드A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera
https://github.com/Pooookeman/ED-sKWS
공식 코드ED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and Energy-Efficient Keyword Spotting
https://github.com/SIP-Lab/Deep-Learning-Mobile
공식 코드Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
https://github.com/Singular-Brain/CCSPNet
공식 코드Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
https://github.com/SmartEngines/bipolar-morphological-resnet
공식 코드https://github.com/UNIC-Lab/LOGNN
공식 코드https://github.com/WhiteByeBye/Post-Quantum-Secure-Blockchained-Federated-Learning
공식 코드Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing
https://github.com/XJTU-Haolin/TT3D
공식 코드https://github.com/XiankeQiang/AdaptiveSplitFederatedLearning
공식 코드Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing
https://github.com/YXShi2000/FedMHO
공식 코드FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
https://github.com/Yip-Jia-Qi/codecformer
공식 코드https://github.com/Yuanliaoo/CG_satellite
공식 코드Towards Routing and Edge Computing in Satellite-Terrestrial Networks: A Column Generation Approach
https://github.com/acfr/cassdetect
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.