Can PEAR Make Deep Learning Easier to Trust?

The potential and constraints of PEAR as a preliminary step toward explaining consensus in deep learning are discussed. The success of improving consensus without reducing explanations to unimportant or uninformative outputs varies depending on the dataset and the hyperparameter selections made, according to the results. Although their inclusion increases the complexity of model tweaking, the dual correlation terms in the loss show to be crucial.