论文标题
关于“深度学习”不当行为
On "Deep Learning" Misconduct
论文作者
论文摘要
这是一篇理论论文，作为同一会议ISAIC 2022的全体会议的同伴论文。与在同一会议上作者的全体谈话相反，有意识学习（Weng，2022b; Weng，2022c）发展了一个生命的单一网络（许多任务），“深度学习”为每个任务训练多个网络。尽管“深度学习”可能使用不同的学习模式，包括受监督，加强和对抗模式，但几乎所有“深度学习”项目显然都遭受了相同的不当行为的困扰，称为“数据删除”和“训练数据测试”。本文建立了一个定理，即一种称为纯猜测最近的邻居（PGNN）的简单方法在验证数据集和测试数据集中达到任何所需的错误，包括零误差要求，通过相同的不当行为，只要测试数据集具有作者的拥有，并且培训的时间和培训时间均有限，但却没有任何东西。不当行为违反了称为透明度和交叉验证的众所周知的方案。不当行为的性质是致命的，因为在没有任何不相交的测试的情况下，“深度学习”显然是不可概括的。
This is a theoretical paper, as a companion paper of the plenary talk for the same conference ISAIC 2022. In contrast to the author's plenary talk in the same conference, conscious learning (Weng, 2022b; Weng, 2022c) which develops a single network for a life (many tasks), "Deep Learning" trains multiple networks for each task. Although "Deep Learning" may use different learning modes, including supervised, reinforcement and adversarial modes, almost all "Deep Learning" projects apparently suffer from the same misconduct, called "data deletion" and "test on training data". This paper establishes a theorem that a simple method called Pure-Guess Nearest Neighbor (PGNN) reaches any required errors on validation data set and test data set, including zero-error requirements, through the same misconduct, as long as the test data set is in the possession of the authors and both the amount of storage space and the time of training are finite but unbounded. The misconduct violates well-known protocols called transparency and cross-validation. The nature of the misconduct is fatal, because in the absence of any disjoint test, "Deep Learning" is clearly not generalizable.
