论文标题
网络中的顺序转移机学习：测量数据和神经净相似性对可传递性的影响
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
论文作者
论文摘要
在面临类似预测任务的独立实体网络中，传输机器学习可以使用分布式数据集重复使用并改善神经网，而无需曝光原始数据。随着业务网络中数据集的数量的增长，并非每个神经净转移都成功，因此需要指标来影响目标性能的转移性。我们对一个由六家不同餐厅的销售数据组成的独特现实用例进行实证研究。我们在这些餐厅销售数据中训练和转移神经网并衡量其可转让性。此外，我们根据数据差异，数据投影和神经净相似性的新指标来计算潜在的可转让性指标。我们获得可转移性和测试指标之间的显着负相关。我们的发现允许根据这些指标选择传输路径，从而改善模型性能，同时需要更少的模型传输。
In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the exposure of raw data. As the number of data sets in business networks grows and not every neural net transfer is successful, indicators are needed for its impact on the target performance-its transferability. We perform an empirical study on a unique real-world use case comprised of sales data from six different restaurants. We train and transfer neural nets across these restaurant sales data and measure their transferability. Moreover, we calculate potential indicators for transferability based on divergences of data, data projections and a novel metric for neural net similarity. We obtain significant negative correlations between the transferability and the tested indicators. Our findings allow to choose the transfer path based on these indicators, which improves model performance whilst simultaneously requiring fewer model transfers.
