Transfer‐learning‐based Raman spectra identification. (28th November 2019) - Zhang, Rui; Xie, Huimin; Cai, Shuning; Hu, Yong; Liu, Guo‐kun; Hong, Wenjing; Tian, Zhong‐qun
Abstract: Deep‐learning‐based spectral identification received intensive interests benefiting from the availability of large scale spectral databases. However, for the identification of spectroscopic data such as Raman, the massive experimental data remained challenging, impeding the application of deep neural networks. Here, we describe a new approach with a transfer‐learning model pretrained on a standard Raman spectral database for the identification of Raman spectra data of organic compounds that are not included in the database and with limited data. Our results show that, with transfer learning, classification accuracy improvement of our convolutional neural network reaches 4.1% and that of our fully connected deep neural network reaches 5.0%. By investigating the influence of the source datasets, we find that our transfer learning method is able to incorporate both relevant and seemingly irrelevant source datasets for pretraining, and the relevant source dataset brings better classification accuracy than that of the seemingly irrelevant source dataset. This study demonstrates that the transfer learning technique has great potential in the effective identification of Raman spectra when the number of Raman data is limited. Abstract : An efficient transfer learning method—fine‐tuning which focuses on taking advantages of the weights pretrained on a large‐scale Raman database was evaluated on a multiclass classification task of small‐scale Raman spectra. Both DNN and CNN exhibited significant better performance when applying this method, indicating the external knowledge learned from standard Raman database raised the model's ability to extract features. Further investigation on the influence of the source datasets shows that even seemingly irrelevant source dataset makes progress on this classification task.