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http://hdl.handle.net/11375/27575
Title: | Predicting Transfer Learning Performance Using Dataset Similarity for Time Series Classification of Human Activity Recognition |
Other Titles: | Transfer Learning Performance Using Dataset Similarity on Realtime Classification |
Authors: | Clark, Ryan |
Advisor: | Doyle, Thomas |
Department: | Biomedical Engineering |
Keywords: | Deep Learning;Machine Learning;Transfer Learning;Similarity;GAN;Signal Classification |
Publication Date: | 2022 |
Abstract: | Deep learning is increasingly becoming a viable way of classifying all types of data. Modern deep learning algorithms, such as one dimensional convolutional neural networks, have demonstrated excellent performance in classifying time series data because of the ability to identify time invariant features. A primary challenge of deep learning for time series classification is the large amount of data required for training and many application domains, such as in medicine, have challenges obtaining sufficient data. Transfer learning is a deep learning method used to apply feature knowledge from one deep learning model to another; this is a powerful tool when both training datasets are similar and offers smaller datasets the power of more robust larger datasets. This makes it vital that the best source dataset is selected when performing transfer learning and presently there is no metric for this purpose. In this thesis a metric of predicting the performance of transfer learning is proposed. To develop this metric this research will focus on classification and transfer learning for human-activity-recognition time series data. For general time series data, finding temporal relations between signals is computationally intensive using non-deep learning techniques. Rather than time-series signal processing, a neural network autoencoder was used to first transform the source and target datasets into a time independent feature space. To compare and quantify the suitability of transfer learning datasets, two metrics were examined: i) average embedded signal from each dataset was used to calculate the distance between each datasets centroid, and ii) a Generative Adversarial Network (GAN) model was trained and the discriminator portion of the GAN is then used to assess the dissimilarity between source and target. This thesis measures a correlation between the distance between two dataset and their similarity, as well as the ability for a GAN to discriminate between two datasets and their similarity. The discriminator metric, however, does suffer from an upper limit of dissimilarity. These metrics were then used to predict the success of transfer learning from one dataset to another for the purpose of general time series classification. |
URI: | http://hdl.handle.net/11375/27575 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Clark_Ryan_A_2022May_MASc.pdf | Ryan Clark Thesis | 1.83 MB | Adobe PDF | View/Open |
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