# Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks

Source: [Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa67ae1e209131cea17c06>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:20:05.971Z

Authors: Christopher A. George, Eduardo A. Barrera, Kenric P. Nelson

Published: 2020-05-29T20:05:06.000Z

## Abstract

We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset. These metrics, referred to as the "geometric accuracy," "decisiveness," and "robustness," are based on the generalized mean ($\\rho$ equals 0, 1, and -2/3, respectively) of the classifier's self-reported and measured probabilities of correct classification. We also propose some minor clarifications to standardize the metric definitions. With these updates, we show some examples of calculating the metrics using deep convolutional neural networks (AlexNet and DenseNet) acting on large datasets (the German Traffic Sign Recognition Benchmark and ImageNet).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.00058>)

[PDF](<https://arxiv.org/pdf/2006.00058.pdf>)