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Found 7 papers, 0 papers with code
CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations
The counterfactuals comprise an increased granularity through minimal feature changes.
Locally Testing Model Detections for Semantic Global Concepts
While global concept encodings generally enable a user to test a model for a specific concept, linking global concept encodings to the local processing of single network inputs reveals their strengths and limitations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
GCPV: Guided Concept Projection Vectors for the Explainable Inspection of CNN Feature Spaces
The latter, though, is of particular interest for debugging, like finding and understanding outliers, learned notions of sub-concepts, and concept confusion.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces
These allow insights into both the flow and likeness of semantic information within CNN layers, and into the degree of their similarity between different network architectures.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability
The guiding use-case is a post-hoc explainability framework for object detection (OD) CNNs, towards which existing concept analysis (CA) methods are successfully adapted.
Dimensionality Reduction
Explainable artificial intelligence
+4
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Artificial Intelligence Methods in In-Cabin Use Cases: A Survey
As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles.
