Core Concepts
Graph-based algorithms offer efficient and meaningful counterfactual explanations for image classifiers.
Abstract
Counterfactuals leverage minimal edits to explain classifier predictions.
Conceptual counterfactuals require semantic relevance for meaningful explanations.
Graph Machine Learning algorithms enhance counterfactual explanation efficiency.
Supervised and unsupervised approaches provide valuable insights on counterfactual explanations.
Evaluation metrics like Precision and NDCG assess the performance of different models.
Stats
"In VG-DENSE experiments, the superior performance of supervised GCN is evident in edit numbers."
"Supervised GNNs necessitate training on ∼N 2/2=70K pairs, maintaining a quadratic relationship with input data."
Quotes
"Semantics are fundamentally essential to meaningful counterfactual explanations."
"Both high-level semantics as well as low-level features can be expressed in the same mathematical format."