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There is an increasing demand for interpretation of model predictionsespecially in high-risk applications. Various visualization approaches havebeen proposed to estimate the part of input which is relevant to a specificmodel prediction. However, most approaches require model structure andparameter details in order to obtain the visualization results, and in generalmuch effort is required to adapt each approach to multiple types of tasksparticularly when model backbone and input format change over tasks. In thisstudy, a simple yet effective visualization framework called PAMI is proposedbased on the observation that deep learning models often aggregate featuresfrom local regions for model predictions. The basic idea is to mask majority ofthe input and use the corresponding model output as the relative contributionof the preserved input part to the original model prediction. For each input,since only a set of model outputs are collected and aggregated, PAMI does notrequire any model detail and can be applied to various prediction tasks withdifferent model backbones and input formats. Extensive experiments on multipletasks confirm the proposed method performs better than existing visualizationapproaches in more precisely finding class-specific input regions, and whenapplied to different model backbones and input formats. The source code will bereleased publicly.

PAMI: partition input and aggregate outputs for model interpretation