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Sparse Subspace Clustering for Concept Discovery (SSCCD)
[article]
2022
arXiv
pre-print
Concepts are key building blocks of higher level human understanding. Explainable AI (XAI) methods have shown tremendous progress in recent years, however, local attribution methods do not allow to identify coherent model behavior across samples and therefore miss this essential component. In this work, we study concept-based explanations and put forward a new definition of concepts as low-dimensional subspaces of hidden feature layers. We novelly apply sparse subspace clustering to discover
arXiv:2203.06043v1
fatcat:yztxyaoxnnefdl74atrhfibgim