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Multi-source domain adaptation on imbalanced data: application to the improvement of chairlifts safety

Abstract : Bluecime has designed a camera-based system to monitor the boarding station of chairlifts in ski resorts, which aims at increasing the safety of all passengers. This already successful system does not use any machine learning component and requires an expensive configuration step. Machine learning is a subfield of artificial intelligence which deals with studying and designing algorithms that can learn and acquire knowledge from examples for a given task. Such a task could be classifying safe or unsafe situations on chairlifts from examples of images already labeled with these two categories, called the training examples. The machine learning algorithm learns a model able to predict one of these two categories on unseen cases. Since 2012, it has been shown that deep learning models are the best suited machine learning models to deal with image classification problems when many training data are available. In this context, this PhD thesis, funded by Bluecime, aims at improving both the cost and the effectiveness of Bluecime's current system using deep learning. We first propose to formalize the Bluecime problem as a classification task with different training settings emulating use cases. We also propose a deep learning baseline providing competitive results in most of the settings, for a low configuration cost. We then propose different approaches to improve our baseline method. First, a data augmentation strategy to improve the robustness of our model. Then, two methods to better optimize the F-measure, a performance measure used in anomaly detection and better suited to evaluate our imbalanced problem than the usual accuracy measure. Finally, we propose selection strategies for the training data to improve results on newly installed chairlift for which no labeled training data is available. With this work we also show negative but interesting results on domain adaption in case of different imbalanced class distributions between the source and target domains.
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Contributor : Kevin Bascol <>
Submitted on : Wednesday, December 18, 2019 - 2:43:56 PM
Last modification on : Monday, January 13, 2020 - 5:46:07 PM
Long-term archiving on: : Thursday, March 19, 2020 - 6:55:50 PM


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  • HAL Id : tel-02417994, version 1


Kevin Bascol. Multi-source domain adaptation on imbalanced data: application to the improvement of chairlifts safety. Machine Learning [cs.LG]. Université jean Monnet, 2019. English. ⟨tel-02417994⟩



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