论文标题
探索基于采矿图像的软件工件中转移学习的功效
Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts
论文作者
论文摘要
转移学习使我们能够训练需要大量学习参数的深度体系结构，即使可用数据的数量受到限制，通过利用先前接受过另一个任务的现有模型来限制。在这里，我们探讨了使用预先培训的模型在非软件工程数据上进行的，应用于软件UML图的问题。我们的实验结果表明，培训对转移学习的反应与样本量相关，即使预先训练的模型未暴露于软件领域的培训实例。我们将转移的网络与其他网络进行对比，以在不同尺寸的培训集上显示出其优势，这表明当没有大量培训数据时，转移学习对自定义深度体系结构同样有效。
Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task. Here we explore the applicability of transfer learning utilizing models pre-trained on non-software engineering data applied to the problem of classifying software UML diagrams. Our experimental results show training reacts positively to transfer learning as related to sample size, even though the pre-trained model was not exposed to training instances from the software domain. We contrast the transferred network with other networks to show its advantage on different sized training sets, which indicates that transfer learning is equally effective to custom deep architectures when large amounts of training data is not available.
