https://github.com/urbangiser/xgeoml
공식 코드An Ensemble Framework for Explainable Geospatial Machine Learning Models
Papers with Code 아카이브
Explainable Artificial Intelligence (XAI)
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Explainable Artificial Intelligence
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vaynexie/CausalX-ViT
공식 코드https://github.com/vectorsss/shapG
공식 코드ShapG: new feature importance method based on the Shapley value
https://github.com/virelay/corelay
공식 코드https://github.com/virelay/virelay
공식 코드https://github.com/visinf/funnybirds
공식 코드FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods
https://github.com/visual-xai-for-time-series/time-series-xai-perturbation-analysis
공식 코드A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI
https://github.com/volesen/slicing-through-bias
공식 코드https://github.com/weinajin/euca
공식 코드https://github.com/weinajin/multimodal_explanation
공식 코드Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis
https://github.com/wickstrom/quantitative-xai-manipulation
공식 코드From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation
https://github.com/x-y-zhao/BayLime
공식 코드BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
https://github.com/xx987/xfbci
공식 코드Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
https://github.com/yaorong0921/hxai-survey
공식 코드Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
https://github.com/yiqiao-yin/YinsRLab
공식 코드https://github.com/yizza91/rfxpl
공식 코드https://github.com/zju-vipa/protopformer
공식 코드https://gitlab.com/rwth-itsec/explainability-analyzed-dga-models
공식 코드https://zenodo.org/record/13844552
공식 코드https://zenodo.org/record/5497609
공식 코드Revealing drivers and risks for power grid frequency stability with explainable AI
https://github.com/11301858/xaisuite
https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
https://github.com/EoinKenny/AAAI-2021
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
https://github.com/FLE-ISP/CEILS
Counterfactual Explanations as Interventions in Latent Space
https://github.com/Imageomics/Prompt_CAM
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
https://github.com/Jh-SYSU/MolRep
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
https://github.com/Mahdidrm/Emotion-Recognition
Explaining Explanations: An Overview of Interpretability of Machine Learning
https://github.com/MarcelRobeer/ContrastiveExplanation
https://github.com/MorenoLaQuatra/ARCH
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
https://github.com/RuoyuChen10/objectdetection-saliency-maps
Interpreting Object-level Foundation Models via Visual Precision Search
https://github.com/YevgenyW/Responsible-DS
https://github.com/aminb99/choquet-integral-NN
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
https://github.com/batmanlab/Ladder
LADDER: Language Driven Slice Discovery and Error Rectification
https://github.com/bmezaris/l-cam
Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism
https://github.com/cohortshapley/cohortintgrad
https://github.com/copenlu/ALPS_2021
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.