https://github.com/satyamahesh84/SIDU_XAI_CODE
공식 코드Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method
Detecting mental disorder on social media: a ChatGPT-augmented explainable approach
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future
Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
Explanations of Black-Box Model Predictions by Contextual Importance and Utility
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences
Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation
Explainability of Machine Learning Models under Missing Data
Explaining Local, Global, And Higher-Order Interactions In Deep Learning
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems
Bounded logit attention: Learning to explain image classifiers
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
Study on the Helpfulness of Explainable Artificial Intelligence
Generative and Explainable AI for High-Dimensional Channel Estimation
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.