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In recent years, an abundance of feature attribution methods for explainingneural networks have been developed. Especially in the field of computervision, many methods for generating saliency maps providing pixel attributionsexist. However, their explanations often contradict each other and it is notclear which explanation to trust. A natural solution to this problem is theaggregation of multiple explanations. We present and compare differentpixel-based aggregation schemes with the goal of generating a new explanation,whose fidelity to the model's decision is higher than each individualexplanation. Using methods from the field of Bayesian Optimization, weincorporate the variance between the individual explanations into theaggregation process. Additionally, we analyze the effect of multiplenormalization techniques on ensemble aggregation.

Fidelity of Ensemble Aggregation for Saliency Map Explanations using Bayesian Optimization Techniques