There has been increasing interest in building deep hierarchy-aware
classifiers that aim to quantify and reduce the severity of mistakes, and not
just reduce the number of errors. The idea is to exploit the label hierarchy
(e.g., the 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 always show practical improvement over the standard
cross-entropy baseline in making better mistakes. The reason for the reduction
in average mistake-severity can be attributed to the increase in low-severity
mistakes, which may also explain the noticeable drop in their accuracy. To this
end, we use 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 hyperparameters and requires adding just a
few lines 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. CRM, because of its simplicity, can be used with
any off-the-shelf trained model that provides reliable likelihood estimates.

研究表明，当前基于层次结构的分类器不能在减少深度学习分类器中较严重错误方面表现出实用的改进。然而，条件风险最小化（CRM）框架作为一种简单又有效的方法，通过修正推理时的错误来修复此问题，同时避免引入过多的超参数或更多的代码。

深度网络中测试时间无成本似然操纵以减小错误率

No Cost Likelihood Manipulation at Test Time for Making Better Mistakes  in Deep Networks

Deep neural networks have improved image classification dramatically over the
past decade, but have done so by focusing on performance measures that treat
all classes other than the ground truth as equally wrong. This has led to a
situation in which mistakes are less likely to be made than before, but are
equally likely to be absurd or catastrophic when they do occur. Past works have
recognised and tried to address this issue of mistake severity, often by using
graph distances in class hierarchies, but this has largely been neglected since
the advent of the current deep learning era in computer vision. In this paper,
we aim to renew interest in this problem by reviewing past approaches and
proposing two simple modifications of the cross-entropy loss which outperform
the prior art under several metrics on two large datasets with complex class
hierarchies: tieredImageNet and iNaturalist'19.

本篇论文提出了使用图距离和交叉熵损失的两种简单方法来解决深度神经网络中错误严重性的问题，并在两个大型具有复杂类层次结构的数据集（tieredImageNet 和 iNaturalist'19）上优于先前的方法。