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Privacy Preserving

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코드

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

https://github.com/wenbin-lin/OcclusionFusion

공식 코드

OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction

https://github.com/wendongbi/KT-GNN

공식 코드

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

https://github.com/wendongbi/MMGNN

공식 코드

MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution

https://github.com/wendongbi/th-gnn

공식 코드

Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks

https://github.com/wenkehuang/fccl

공식 코드

Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning

https://github.com/wenkehuang/marsfl

공식 코드

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

https://github.com/wenqi006/slidegraph

공식 코드

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer

https://github.com/wenqifan03/GraphRec-WWW19

공식 코드

Graph Neural Networks for Social Recommendation

https://github.com/wentao-xu/shgnn

공식 코드

SHGNN: Structure-Aware Heterogeneous Graph Neural Network

https://github.com/wenweigu/isolate

공식 코드

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

https://github.com/westny/mtp-go

공식 코드

Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

https://github.com/wfwf10/feature-based-federated-transfer-learning

공식 코드

Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning

https://github.com/whuminghui/neunet

공식 코드

Joint Learning Neuronal Skeleton and Brain Circuit Topology with Permutation Invariant Encoders for Neuron Classification

https://github.com/whuzjw/privacy-reid

공식 코드

Learnable Privacy-Preserving Anonymization for Pedestrian Images

https://github.com/wicg/privacy-preserving-ads

공식 코드

Masked LARk: Masked Learning, Aggregation and Reporting worKflow

https://github.com/williamluo0/stableprivatelora

공식 코드

Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models

https://github.com/williamweiwu/williamweiwu.github.io

공식 코드

Hashing-Accelerated Graph Neural Networks for Link Prediction

https://github.com/wnma3mz/fedlmd

공식 코드

Federated Learning with Label-Masking Distillation

https://github.com/wnn2000/fedaaai

공식 코드

FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

https://github.com/wnn2000/fediic

공식 코드

FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification

https://github.com/wnn2000/fednoro

공식 코드

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity

https://github.com/wonder7racer/s2fgl

공식 코드

S2FGL: Spatial Spectral Federated Graph Learning

https://github.com/wondercs1213/uniea

공식 코드

Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning

https://github.com/woodcutter1998/gdgnn

공식 코드

Geodesic Graph Neural Network for Efficient Graph Representation Learning

https://github.com/wouterwln/neuralfactortrees

공식 코드

Node Classification in Random Trees

https://github.com/wshi83/medadapter

공식 코드

MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning

https://github.com/wurenzhi/hyper_label_model

공식 코드

Learning Hyper Label Model for Programmatic Weak Supervision

https://github.com/wusw14/gnn-in-rs

공식 코드

Graph Neural Networks in Recommender Systems: A Survey

https://github.com/wuyxin/falcon-glfrontiers

공식 코드

Efficient Automatic Machine Learning via Design Graphs

https://github.com/wuyxin/graphmetro

공식 코드

GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts

https://github.com/wuzhenyusjtu/Privacy-AdversarialLearning

공식 코드

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

https://github.com/wwbrannon/congrat

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ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings

https://github.com/wwh0411/knowledgesg

공식 코드

KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server

https://github.com/wyshi/lm_privacy

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Selective Differential Privacy for Language Modeling

https://github.com/xalentis/syntheticeeg

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A Statistical Approach for Synthetic EEG Data Generation

https://github.com/xhcdream/kcgn

공식 코드

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

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