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Evaluating the robustness of ML models to out-of-distribution data through similarity analysis



Publication Type:

Conference/Workshop Paper


1st Workshop on Advanced AI Techniques for Data Management and Analytics




In Machine Learning systems, several factors impact the performance of a trained model. The most important ones include model architecture, the amount of training time, the dataset size and diversity. We present a method for analyzing datasets from a use-case scenario perspective, detecting and quantifying out-of-distribution (OOD) data on dataset level.Our main contribution is the novel use of similarity metrics for the evaluation of the robustness of a model by introducing relative Fréchet Inception Distance (FID) and relative Kernel Inception Distance (KID) measures. These relative measures are relative to a baseline in-distribution dataset and are used to estimate how the model will perform on OOD data (i.e. estimate the model accuracy drop). We find a correlation between our proposed relative FID/relative KID measure and the drop in Average Precision (AP) accuracy on unseen data.


author = {Joakim Lind{\'e}n and H{\aa}kan Forsberg and Ingemar S{\"o}derquist and Masoud Daneshtalab},
title = {Evaluating the robustness of ML models to out-of-distribution data through similarity analysis},
number = {1},
month = {September},
year = {2023},
booktitle = {1st Workshop on Advanced AI Techniques for Data Management and Analytics},
publisher = {Springer},
url = {}