基于深度迁移学习进行时间序列分类

标题

Transfer learning for time series classification

作者

Fawaz Hassan Ismail,Forestier Germain,Weber Jonathan,Idoumghar Lhassane,Muller Pierre-Alain

下载地址

https://arxiv.org/abs/1811.01533

发布时间

2018/11/5

摘要

Transfer learning for deep neural networks is the process of first training a
base network on a source dataset, and then transferring the learned features
(the network's weights) to a second network to be trained on a target dataset.
This idea has been shown to improve deep neural network's generalization
capabilities in many computer vision tasks such as image recognition and object
localization. Apart from these applications, deep Convolutional Neural Networks
(CNNs) have also recently gained popularity in the Time Series Classification
(TSC) community. However, unlike for image recognition problems, transfer
learning techniques have not yet been investigated thoroughly for the TSC task.
This is surprising as the accuracy of deep learning models for TSC could
potentially be improved if the model is fine-tuned from a pre-trained neural
network instead of training it from scratch. In this paper, we fill this gap by
investigating how to transfer deep CNNs for the TSC task. To evaluate the
potential of transfer learning, we performed extensive experiments using the
UCR archive which is the largest publicly available TSC benchmark containing 85
datasets. For each dataset in the archive, we pre-trained a model and then
fine-tuned it on the other datasets resulting in 7140 different deep neural
networks. These experiments revealed that transfer learning can improve or
degrade the model's predictions depending on the dataset used for transfer.
Therefore, in an effort to predict the best source dataset for a given target
dataset, we propose a new method relying on Dynamic Time Warping to measure
inter-datasets similarities. We describe how our method can guide the transfer
to choose the best source dataset leading to an improvement in accuracy on 71
out of 85 datasets.

推荐理由

Mr-UC :

迁移学习和深度学习已经被广泛应用于计算机视觉和自然语言处理领域。但是在时间序列分类方面，至今没有完整的有代表性的工作。这是第一篇系统探讨基于深度迁移学习进行时间序列分类的论文。