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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/suakaw/bdq_privacyar

공식 코드

Privacy-Preserving Action Recognition via Motion Difference Quantization

https://github.com/sukwonyun/s-mixup

공식 코드

S-Mixup: Structural Mixup for Graph Neural Networks

https://github.com/sunilkmaurya/FSGNN

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Improving Graph Neural Networks with Simple Architecture Design

https://github.com/superlouis/NODE-SELECT

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NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique

https://github.com/superstar1225/DensePose_from_WiFi

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DensePose From WiFi

https://github.com/swapnil-saha/NMF_DP

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Privacy-preserving Non-negative Matrix Factorization with Outliers

https://github.com/swsamleo/mlstgcn

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Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

https://github.com/swtheing/imc-gae

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Inductive Matrix Completion Using Graph Autoencoder

https://github.com/syon-li/depwignn

공식 코드

DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text

https://github.com/taco-group/facelock

공식 코드

Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing

https://github.com/taihasegawa/degnn

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DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise

https://github.com/tailvyuanliang/hgwavenet

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HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction

https://github.com/takuo-h/GNN-for-OOKB

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Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

https://github.com/talbenha/cell-tracker-gnn

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Graph Neural Network for Cell Tracking in Microscopy Videos

https://github.com/tamlhp/awesome-privex

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A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

https://github.com/tanfiona/causeeffectdetection

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NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection

https://github.com/tangxianfeng/PA-GNN

공식 코드

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

https://github.com/tanqiujiang/rapid

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RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models

https://github.com/tanwimallick/TL-DCRNN

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Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

https://github.com/taokz/FedR

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Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

https://github.com/taoqi98/fairvfl

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FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

https://github.com/taowen0309/ttg-nn

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Tensor-view Topological Graph Neural Network

https://github.com/tata1661/shine-emnlp21

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Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification

https://github.com/tayebiarasteh/chestx

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Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels

https://github.com/tayebiarasteh/dp_cxr

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

https://github.com/teawizardry/schrodingers-camera

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Schrödinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera

https://github.com/tech-srl/bottleneck

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On the Bottleneck of Graph Neural Networks and its Practical Implications

https://github.com/tecsar-uncc/pheva

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PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

https://github.com/tecsar-uncc/poselift

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Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark

https://github.com/tedzhouhk/sedyt

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SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings

https://github.com/teijyogen/fl-market

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FL-Market: Trading Private Models in Federated Learning

https://github.com/teimour-halizadeh/privacy_data_driven_control

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Preserving Privacy in Cloud-based Data-Driven Stabilization

https://github.com/temp-gimlab/gnnsampler

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GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware

https://github.com/tencent-ailab/DrugOOD

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DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations

https://github.com/tencent-alchemy/Alchemy

공식 코드

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

https://github.com/tgebhart/sheaf_kg_transind

공식 코드

Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference

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