A Principled Evaluation Protocol for Comparative Investigation of the
Effectiveness of DNN Classification Models on Similar-but-non-identical
Datasets
- URL: http://arxiv.org/abs/2209.01848v1
- Date: Mon, 5 Sep 2022 09:14:43 GMT
- Title: A Principled Evaluation Protocol for Comparative Investigation of the
Effectiveness of DNN Classification Models on Similar-but-non-identical
Datasets
- Authors: Esla Timothy Anzaku, Haohan Wang, Arnout Van Messem, Wesley De Neve
- Abstract summary: We show that Deep Neural Network (DNN) models show significant, consistent, and largely unexplained degradation in accuracy on replication test datasets.
We propose a principled evaluation protocol that is suitable for performing comparative investigations of the accuracy of a DNN model on multiple test datasets.
Our experimental results indicate that the observed accuracy degradation between established benchmark datasets and their replications is consistently lower.
- Score: 11.735794237408427
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Deep Neural Network (DNN) models are increasingly evaluated using new
replication test datasets, which have been carefully created to be similar to
older and popular benchmark datasets. However, running counter to expectations,
DNN classification models show significant, consistent, and largely unexplained
degradation in accuracy on these replication test datasets. While the popular
evaluation approach is to assess the accuracy of a model by making use of all
the datapoints available in the respective test datasets, we argue that doing
so hinders us from adequately capturing the behavior of DNN models and from
having realistic expectations about their accuracy. Therefore, we propose a
principled evaluation protocol that is suitable for performing comparative
investigations of the accuracy of a DNN model on multiple test datasets,
leveraging subsets of datapoints that can be selected using different criteria,
including uncertainty-related information. By making use of this new evaluation
protocol, we determined the accuracy of $564$ DNN models on both (1) the
CIFAR-10 and ImageNet datasets and (2) their replication datasets. Our
experimental results indicate that the observed accuracy degradation between
established benchmark datasets and their replications is consistently lower
(that is, models do perform better on the replication test datasets) than the
accuracy degradation reported in published works, with these published works
relying on conventional evaluation approaches that do not utilize
uncertainty-related information.
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