论文标题
自动伽马光谱识别中的转移学习
Transfer Learning in Automated Gamma Spectral Identification
论文作者
论文摘要
创建的旨在对时间序列的伽玛射线光谱进行自动同位素分类而创建的先前训练的卷积神经网络（CNN）的模型和权重被用来提供源域知识作为对潜在感兴趣的新领域的培训。以前的结果仅使用建模的光谱数据来实现。在这项工作中，我们试图将获得的知识转移到仅测量数据的新的（即使是相似的）领域。在对该问题空间的任何成功的数据驱动方法中，对建模数据进行训练并预测测量数据的能力至关重要。
The models and weights of prior trained Convolutional Neural Networks (CNN) created to perform automated isotopic classification of time-sequenced gamma-ray spectra, were utilized to provide source domain knowledge as training on new domains of potential interest. The previous results were achieved solely using modeled spectral data. In this work we attempt to transfer the knowledge gained to the new, if similar, domain of solely measured data. The ability to train on modeled data and predict on measured data will be crucial in any successful data-driven approach to this problem space.
