Can test input selection methods for deep neural network guarantee test diversity? A large-scale empirical study

Abstract

Recently, various methods on test input selection for deep neural network (TIS-DNN) have been proposed. These methods can effectively reduce the labeling cost by selecting a subset from the original test inputs, which can still accurately estimate the performance (such as accuracy) of the target DNN models.