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Traditional evaluation metrics for learned models that report aggregatescores over a test set are insufficient for surfacing important and informativepatterns of failure over features and instances. We introduce and study amethod aimed at characterizing and explaining failures by identifying visualattributes whose presence or absence results in poor performance. Indistinction to previous work that relies upon crowdsourced labels for visualattributes, we leverage the representation of a separate robust model toextract interpretable features and then harness these features to identifyfailure modes. We further propose a visualization method aimed at enablinghumans to understand the meaning encoded in such features and we test thecomprehensibility of the features. An evaluation of the methods on the ImageNetdataset demonstrates that: (i) the proposed workflow is effective fordiscovering important failure modes, (ii) the visualization techniques helphumans to understand the extracted features, and (iii) the extracted insightscan assist engineers with error analysis and debugging.

Understanding Failures of Deep Networks via Robust Feature Extraction