论文标题
分形维度
Fractal Dimension Generalization Measure
论文作者
论文摘要
为机器学习模型的性能制定强大的概括度量是一项重要且具有挑战性的任务。在预测概括时，该地区的许多最新研究都集中在模型决策边界上。在本文中，作为“预测深度学习中的概括”竞争的一部分，我们使用分形维度的概念分析了决策边界的复杂性，并基于该技术制定了概括度量。
Developing a robust generalization measure for the performance of machine learning models is an important and challenging task. A lot of recent research in the area focuses on the model decision boundary when predicting generalization. In this paper, as part of the "Predicting Generalization in Deep Learning" competition, we analyse the complexity of decision boundaries using the concept of fractal dimension and develop a generalization measure based on that technique.
