Publikation
A Global Dataset-Specific Any-Order Minimal Expectation Baseline for Saliency Scores
Leonid Schwenke; Martin Atzmueller
In: Proc. IEEE International Conference on Data Science and Advanced Analytics, DSAA 2025. International Conference on Data Science and Advanced Analytics (DSAA), Pages 1-10, IEEE, 2025.
Zusammenfassung
A prominent type of explanation for neural networks are saliency/attribution-based approaches, which highlight the most relevant inputs. Here, recent works suggest sub-optimality of those methods and emphasize the challenge of evaluation. In this paper, we present a new dataset-relative baseline to assess the minimal expectations on saliency scores, leading towards a new any-order interpretation evaluation framework. Using the Global Coherence Representation (GCR), we propose the SimpleGCR as an implementation of this framework acting as a stable minimal performance baseline. It thus enables a reference point for comparing different explainability metrics. We evaluate our proposed approach by applying a set of current saliency methods on the univariate UCR UEA time series datasets, and demonstrate the sub-optimality of those methods in this context.