Learning from Badly Behaving Data

Lead Research Organisation: Bangor University
Department Name: Sch of Computer Science & Electronic Eng

Abstract

Focusing on deep learning systems, this PhD will investigate the modern data challenge of data "behaving badly". In addition to coming in massive volumes, data can be streaming, drifting, partially labelled, multi-labelled, contaminated, imbalanced, wide, and so on. A prime example of considerable interest is image and video analysis where the same object, person, or animal must be detected, learned, identified and then re-identified in the subsequent image collection or video stream. To solve this problem, we should look into semi-supervised learning in the presence of concept drift, adaptive learning, transductive learning, and more. Deep learning neural networks may prove valuable at the stages when large labelled data sets have been accumulated. Given that multiple objects of interest may be present within the same image, methods from the area of restricted set classification should be explored. This project will seek to offer novel and effective solutions for "badly behaving" data. Where possible, we will aspire to offer theoretical grounds for those solutions to ensure transferability across application domains. A curious potential application is identification of individual animals in a herd or a group for the purposes of non-invasive monitoring. Such an application will cross over to the area of environmental studies, specifically ecosystem conservation and behavioural ecology.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 01/04/2019 30/09/2027
2431066 Studentship EP/S023992/1 01/10/2020 30/09/2024 Franciszek Krzyzowski