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Privacy Preserving

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

https://github.com/ShunChengWu/SceneGraphFusion

공식 코드

SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

https://github.com/Sinwang404/DeepDDs

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DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

https://github.com/SophieZheng998/MaskDroid

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MASKDROID: Robust Android Malware Detection with Masked Graph Representations

https://github.com/SpaceLearner/SessionRec-pytorch

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Learning Multi-granularity User Intent Unit for Session-based Recommendation

https://github.com/SsGood/NFGNN

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Node-oriented Spectral Filtering for Graph Neural Networks

https://github.com/StrohmayerJ/wallhack1.8k

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Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition

https://github.com/SunJiamei/few-shot-lrp-guided

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Explanation-Guided Training for Cross-Domain Few-Shot Classification

https://github.com/THUDM/CogQA

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Cognitive Graph for Multi-Hop Reading Comprehension at Scale

https://github.com/THUDM/GCC

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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

https://github.com/TUM-AIMED/2.5DAttention

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Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

https://github.com/TUM-AIMED/PrivateSegmentation

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Differentially private federated deep learning for multi-site medical image segmentation

https://github.com/Tencent/TFace

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Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

https://github.com/TencentGameMate/gnn_cross_lingual

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Cross-lingual Text Classification with Heterogeneous Graph Neural Network

https://github.com/TheLeprechaun25/neural-improvement-heuristics

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Neural Improvement Heuristics for Graph Combinatorial Optimization Problems

https://github.com/TianwangJia/FedBS

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Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

https://github.com/TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate

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Defending against Backdoors in Federated Learning with Robust Learning Rate

https://github.com/TmacMai/ARGF_multimodal_fusion

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Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion

https://github.com/TsingZ0/GPFL

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GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

https://github.com/UNIC-Lab/LOGNN

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Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

https://github.com/USC-InfoLab/NeuroGNN

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Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data

https://github.com/USC-InfoLab/busyness-graph-neural-network

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Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

https://github.com/VITA-Group/Deep_GCN_Benchmarking

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EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model

https://github.com/VITA-Group/PA-HMDB51

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Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

https://github.com/VITA-Group/Privacy-AdversarialLearning

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Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

https://github.com/VITA-Group/VGAI

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Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

https://github.com/VachelHU/ESGRN

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Time-Series Event Prediction with Evolutionary State Graph

https://github.com/VachelHU/EvoNet

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Time-Series Event Prediction with Evolutionary State Graph

https://github.com/VeritasXu/Ternary-Federated

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Ternary Compression for Communication-Efficient Federated Learning

https://github.com/VishnuDuttSharma/deep-multirobot-task

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Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection

https://github.com/WangXuhongCN/OCGNN

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One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks

https://github.com/WeihengZ/Explainable-Graph-Autoformer

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Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting

https://github.com/WeijingShi/Point-GNN

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Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

https://github.com/WenjieDu/PyPOTS

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Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

https://github.com/WuDan0399/InkStream

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InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update

https://github.com/XL2248/HGNN

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Emotional Conversation Generation with Heterogeneous Graph Neural Network

https://github.com/Xiangyi1996/PPNet-PyTorch

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Part-aware Prototype Network for Few-shot Semantic Segmentation

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