This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the community over the past decade. We generalize the formulation of classification margins from classical research to latest DNNs, summarize theoretical connections between the margin, network generalization, and robustness, and introduce recent efforts in enlarging the margins for DNNs comprehensively. Since the viewpoint of different methods is discrepant, we categorize them into groups for ease of comparison and discussion in the paper. Hopefully, our discussions and overview inspire new research work in the community that aim to improve the performance of DNNs, and we also point to directions where the large margin principle can be verified to provide theoretical evidence why certain regularizations for DNNs function well in practice. We managed to shorten the paper such that the crucial spirit of large margin learning and related methods are better emphasized.

翻译：本文件调查了大幅度培训及其理论基础的最新进展,主要是(非线性)深神经网络(DNNs),这些网络可能是过去十年来社区大规模数据最突出的机器学习模式。我们从古典研究到最新的DNNs的分类边距拟订,总结边际、网络化和稳健性之间的理论联系,并介绍最近全面扩大DNs边际的边际的努力。由于不同方法的观点不尽相同,我们将它们分类为小组,以便于比较和在文件中进行讨论。希望我们的讨论和概览激发社区新的研究工作,目的是改善DNNs的业绩,我们还指出在哪些方面可以核实大边际原则,以提供理论证据说明为什么DNes的某些正规化在实践中运作良好。我们设法缩短了文件的篇幅,以便更好地强调大边际学习和相关方法的关键精神。