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Variant Approach for Identifying Spurious Relations That Deep Learning Models Learn
2021
Frontiers in Water
A deep learning (DL) model learns a function relating a set of input variables with a set of target variables. While the representation of this function in form of the DL model often lacks interpretability, several interpretation methods exist that provide descriptions of the function (e.g., measures of feature importance). On the one hand, these descriptions may build trust in the model or reveal its limitations. On the other hand, they may lead to new scientific understanding. In any case, a
doi:10.3389/frwa.2021.745563
fatcat:5ftw2ufffnd4jl7bjubxc6uoyq