论文标题
通过公制学习预测深度学习的概括-PGDL共享任务
Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task
论文作者
论文摘要
竞争“预测深度学习中的概括（PGDL）”旨在为深度学习模型的概括提供一个平台，并提供对理解和解释这些模型的进展的见解。该报告介绍了用户\ emph {smeznar}提交的解决方案，该解决方案在比赛中获得了八个位置。在提出的方法中，我们创建简单的指标，并在提供的数据集中找到自动测试的最佳组合，并探索如何将输入神经网络体系结构的各种属性组合用于预测其概括。
The competition "Predicting Generalization in Deep Learning (PGDL)" aims to provide a platform for rigorous study of generalization of deep learning models and offer insight into the progress of understanding and explaining these models. This report presents the solution that was submitted by the user \emph{smeznar} which achieved the eight place in the competition. In the proposed approach, we create simple metrics and find their best combination with automatic testing on the provided dataset, exploring how combinations of various properties of the input neural network architectures can be used for the prediction of their generalization.
