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.

本论文阐述了深度学习中的两项不当行为 —— 数据删除和训练集上测试，指出这样的行为会导致性能数据被虚高；提出了一种名为 Nearest Neighbor With Threshold 的简单分类方法，证明该方法使用上述不当行为在拥有有限但无界存储空间和训练时间的情况下，在任何验证集和测试集上都可以达到零误差，但深度学习方法普遍无法通过真实测试集进行检验，证据超出了本文的范围。