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arXiv · 2012.08642

Does the dataset meet your expectations? Explaining sample representation in image data

Dhasarathy Parthasarathy·Anton Johansson
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Abstract

Since the behavior of a neural network model is adversely affected by a lack of diversity in training data, we present a method that identifies and explains such deficiencies. When a dataset is labeled, we note that annotations alone are capable of providing a human interpretable summary of sample diversity. This allows explaining any lack of diversity as the mismatch found when comparing the \textit{actual} distribution of annotations in the dataset with an \textit{expected} distribution of annotations, specified manually to capture essential label diversity. While, in many practical cases, labeling (samples $\rightarrow$ annotations) is expensive, its inverse, simulation (annotations $\rightarrow$ samples) can be cheaper. By mapping the expected distribution of annotations into test samples using parametric simulation, we present a method that explains sample representation using the mismatch in diversity between simulated and collected data. We then apply the method to examine a dataset of geometric shapes to qualitatively and quantitatively explain sample representation in terms of comprehensible aspects such as size, position, and pixel brightness.

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Dhasarathy Parthasarathy, Anton Johansson. 2020-12-06. Does the dataset meet your expectations? Explaining sample representation in image data. https://arxiv.org/abs/2012.08642

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