论文标题
为什么深度学习的绩效数据具有误导性
Why Deep Learning's Performance Data Are Misleading
论文作者
论文摘要
这是一篇理论论文，作为在同一会议AIEE 2023上的主题演讲的同伴论文。与有意识的学习相比，AI中的许多项目都采用了所谓的“深度学习”，其中许多似乎都带来了令人印象深刻的表现。本文解释说，由于两个不当行为，此类绩效数据被欺骗性地膨胀：“数据删除”和“训练集测试”。本文阐明了深度学习中的“数据删除”和“训练集测试”以及为什么它们是不当行为。定义了一种简单的分类方法，称为阈值最近的邻居（NNWT）。一个定理是，只要测试集属于作者，NNWT方法在任何验证集和使用两个不当行为的任何测试集上都达到零误差，并且培训时间和培训时间都是有限的，但与许多深度学习方法一样无界。但是，许多深度学习方法，例如NNWT方法，都不可推广，因为从未通过真实的测试集对其进行测试。为什么？所谓的“测试集”在训练阶段的选择后步骤中使用。实际上在许多深度学习项目中发生不当行为的证据超出了本文的范围。
This is a theoretical paper, as a companion paper of the keynote talk at the same conference AIEE 2023. In contrast to conscious learning, many projects in AI have employed so-called "deep learning" many of which seemed to give impressive performance. This paper explains that such performance data are deceptively inflated due to two misconducts: "data deletion" and "test on training set". This paper clarifies "data deletion" and "test on training set" in deep learning and why they are misconducts. A simple classification method is defined, called Nearest Neighbor With Threshold (NNWT). A theorem is established that the NNWT method reaches a zero error on any validation set and any test set using the two misconducts, as long as the test set is in the possession of the author and both the amount of storage space and the time of training are finite but unbounded like with many deep learning methods. However, many deep learning methods, like the NNWT method, are all not generalizable since they have never been tested by a true test set. Why? The so-called "test set" was used in the Post-Selection step of the training stage. The evidence that misconducts actually took place in many deep learning projects is beyond the scope of this paper.
