在测试时进行无成本的可能性操纵，以在深度网络中更好地犯错误
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
摘要：
人们对构建深层层次感知分类器越来越感兴趣，这些分类器旨在量化和减少错误的严重性，而不仅仅是减少错误的数量。这个想法是利用标签层次结构（例如，WordNet本体），并将图距离视为错误严重性代理。令人惊讶的是，在检查前1个预测的错误严重性分布时，我们发现当前最先进的层次感知深度分类器并不总是显示出比标准交叉熵基线更好的错误的实际改进。平均错误严重性降低的原因可以归因于低严重性错误的增加，这也可以解释其准确性的明显下降。为此，我们使用经典的条件风险最小化（CRM）框架的层次感知分类。给定一个成本矩阵和一个可靠的似然估计（从训练的网络中获得），CRM只是在推理时修正错误;它不需要额外的超参数，只需要在标准的交叉熵基线上添加几行代码。它的性能显著优于最先进的技术，并且在数据集上始终大幅降低顶级$k$预测的平均层次距离，而准确性损失很小。CRM由于其简单性，可以与任何现成的训练模型一起使用，提供可靠的可能性估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
