https://github.com/continental/local-concept-embeddings
공식 코드Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces
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/copenlu/spanex
공식 코드https://github.com/copenlu/tx-ray
공식 코드TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
https://github.com/cristianopatricio/explainable-deep-learning-methods-in-medical-image-classification-a-survey
공식 코드Explainable Deep Learning Methods in Medical Image Classification: A Survey
https://github.com/cwangrun/MGProto
공식 코드https://github.com/cwangrun/ST-ProtoPNet
공식 코드Learning Support and Trivial Prototypes for Interpretable Image Classification
https://github.com/d-becking/ECQx
공식 코드ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
https://github.com/dd1github/xai_for_imbalanced_learning
공식 코드https://github.com/deezer/concept_hierarchy
공식 코드https://github.com/delftcrowd/iui2025_convxai
공식 코드Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
https://github.com/dependable-intelligent-systems-lab/xwhy
공식 코드https://github.com/digitalphonetics/intrinsic-subgraph-generation-for-vqa
공식 코드Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
https://github.com/dmitrykazhdan/gci
공식 코드https://github.com/dorienh/bitcoin_synthesizer
공식 코드https://github.com/drchangliu/xai
공식 코드Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods
https://github.com/drumpt/timing
공식 코드TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
https://github.com/dswatson/rational_shapley
공식 코드https://github.com/e-delaney/cfe_images_how_people_differ_from_machines
공식 코드Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
https://github.com/ece7048/3dsulci
공식 코드https://github.com/eclique/RISE
공식 코드RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/edahelsinki/explainreduce
공식 코드https://github.com/ennosigaeon/xautoml
공식 코드XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
https://github.com/ericotjo001/explainable_ai
공식 코드Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI
https://github.com/esl-epfl/cross-domain-saliency-maps
공식 코드Time series saliency maps: explaining models across multiple domains
https://github.com/esl-epfl/cross-domain-saliency-maps-paper
공식 코드Time series saliency maps: explaining models across multiple domains
https://github.com/etjoa003/explainable_ai
공식 코드Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset
https://github.com/explainingai/fidelity_metrics
공식 코드https://github.com/explainingai/stability
공식 코드Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity
https://github.com/fabien-couthouis/xai-in-rl
공식 코드https://github.com/fair4hep/xai4toptagger
공식 코드A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
https://github.com/fanny-jourdan/cockatiel
공식 코드https://github.com/fau-is/gam_comparison
공식 코드https://github.com/faysal-md/an-interpretable-deep-learning-approach-for-skin-cancer-categorization-ieee2023
공식 코드An Interpretable Deep Learning Approach for Skin Cancer Categorization
https://github.com/ffattr/ffa
공식 코드https://github.com/flaat/llm-graph-cf
공식 코드Natural Language Counterfactual Explanations for Graphs Using Large Language Models
https://github.com/francesco-sovrano/from-philosophy-to-interfaces-an-explanatory-method-and-a-tool-inspired-by-achinstein-s-theory-of-e
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.