Identifying the Sources of Uncertainty in Object Classification

Luis Armando Pérez Rey, Berk İşler, Mike Holenderski, Dmitri Jarnikov

Keywords: Classification, Interpretability, Disentangled Representations, Uncertainty Estimation

Abstract: In image-based object classification, the visual appearance of objects determines which class they are assigned to. External variables that are independent of the object, such as the perspective or the lighting conditions, can modify the object's appearance resulting in ambiguous images that lead to misclassifications. Previous work has proposed methods for estimating the uncertainty of predictions and measure their confidence. However, such methods do not indicate which variables are the potential sources that cause uncertainty. In this paper, we propose a method for image-based object classification that uses disentangled representations to indicate which are the external variables that contribute the most to the uncertainty of the predictions. This information can be used to identify the external variables that should be modified to decrease the uncertainty and improve the classification.

One-sentence Summary: In this work we propose a method for the identification of the external factors that affect the confidence of predictions in image-based object classification via the use of disentangled representations.

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