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Amending Mistakes Post-hoc in Deep Networks by Leveraging Class Hierarchies

There has been increasing interest in building deep hierarchy-aware classifiers, aiming to quantify and reduce the severity of mistakes and not just count the number of errors. The idea is to exploit the label hierarchy (e.g., WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not show practical improvement in making better mistakes than the standard cross-entropy baseline. In fact, they reduce the average mistake-severity metric by largely making additional low-severity or easily avoidable mistakes. This might explain the noticeable accuracy drop. To this end, we resort to the classical Conditional Risk Minimization (CRM) framework for hierarchy aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra parameters; it requires adding just one line of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top-k predictions across datasets, with very little loss in accuracy. Since CRM does not require retraining or fine-tuning of any hyperparameter, it can be used with any off-the-shelf cross-entropy trained model.

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