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Training a deep neural network (DNN) often involves stochastic optimization,which means each run will produce a different model. Several works suggest thisvariability is negligible when models have the same performance, which in thecase of classification is test accuracy. However, models with similar testaccuracy may not be computing the same function. We propose a new measure ofcloseness between classification models based on the output of the networkbefore thresholding. Our measure is based on a robust hypothesis-testingframework and can be adapted to other quantities derived from trained models.

Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks