Why Deep Learning's Performance Data Are Misleading

This is a theoretical paper, as a companion paper of the keynote talk at the same conference. In contrast to conscious learning, many projects in AI have employed deep learning many of which seem to give impressive performance data. This paper explains that such performance data are probably misleadingly inflated due to two possible misconducts: data deletion and test on training set. This paper clarifies what is data deletion in deep learning and what is 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 Post-Selections, 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, like many deep learning methods, the NNWT method has little generalization power. The evidence that misconducts actually took place in many deep learning projects is beyond the scope of this paper. Without a transparent account about freedom from Post-Selections, deep learning data are misleading.