https://github.com/hturbe/protosvit
공식 코드ProtoS-ViT: Visual foundation models for sparse self-explainable classifications
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.
ProtoS-ViT: Visual foundation models for sparse self-explainable classifications
Causal Discovery and Classification Using Lempel-Ziv Complexity
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine
Unified Explanations in Machine Learning Models: A Perturbation Approach
Why model why? Assessing the strengths and limitations of LIME
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
Data Science Principles for Interpretable and Explainable AI
Automatic Extraction of Linguistic Description from Fuzzy Rule Base
Applying Genetic Programming to Improve Interpretability in Machine Learning Models
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
EXTRA: Explanation Ranking Datasets for Explainable Recommendation
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.