While the success of deep learning is commonly attributed to its theoretical equivalence with Support Vector Machines (SVM), the practical implications of this relationship have not been thoroughly explored. This paper pioneers an exploration in this domain, specifically focusing on the identification of Deep Support Vectors (DSVs) within deep learning models. We introduce the concept of DeepKKT conditions, an adaptation of the traditional Karush-Kuhn-Tucker (KKT) conditions tailored for deep learning. Through empirical investigations, we illustrate that DSVs exhibit similarities to support vectors in SVM, offering a tangible method to interpret the decision-making criteria of models. Additionally, our findings demonstrate that models can be effectively reconstructed using DSVs, resembling the process in SVM. The code will be available.

中文翻译：

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深度支持向量

虽然深度学习的成功通常归因于其与支持向量机 (SVM) 的理论等效性，但这种关系的实际含义尚未得到彻底探讨。本文开创了该领域的探索，特别关注深度学习模型中深度支持向量（DSV）的识别。我们引入了 DeepKKT 条件的概念，它是针对深度学习量身定制的传统 Karush-Kuhn-Tucker (KKT) 条件的改编。通过实证研究，我们证明 DSV 与 SVM 中的支持向量有相似之处，为解释模型的决策标准提供了一种切实可行的方法。此外，我们的研究结果表明，可以使用 DSV 有效地重建模型，类似于 SVM 中的过程。该代码将可用。