Self-InterpretableModelwithTransformation EquivariantInterpretation

Withthe proliferation ofmachine learning applications inthe real world, the demand for explaining machine learning predictions continues to grow especially in high-stakes fields. Recent studies havefound that interpretation methods can be sensitive and unreliable, where the interpretations can be disturbed by perturbations or transformations of input data. To address this issue, we propose to learn robust interpretations through transformation equivariant regularization in a self-interpretable model.