Deep learning and data science

Melbourne Centre for Data Science

Deep learning. Data Science. Semi-supervised learning. Machine Learning. Urban design. Computer vision. All in two brief talks!

Melbourne Connect, Level 7, Manhari Room

Melbourne Centre for Data Science is bringing back the popular &quot;Data Science across Disciplines&quot; series. This session focuses on deep learning and data science.Hear from experts in mathematics and urban design._Talks:Selection strategies for labelling within a dataset<a href="https://science.unimelb.edu.au/mcds/who-we-are/evelyn-mannix" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Evelyn Mannix, Melbourne Centre for Data Science</a>In numerous machine learning applications, labeling large image datasets can be an arduous and time-consuming task. Although research has shown that semi-supervised learning techniques can achieve high accuracy with very few labels, little attention has been given to how images within a dataset should be selected for labeling. In this talk, they propose a solution to the challenge of selecting an informative subset to label initially, known as the cold-start problem. They test our approach using several publicly available datasets, namely CIFAR10, Imagenette, DeepWeeds, and EuroSAT, and observe significant improvements in accuracy using semi-supervised learning when our label selection strategy is employed, in comparison to random sampling.Self-supervised computer vision for scalable urban analysis<a href="https://findanexpert.unimelb.edu.au/profile/879485-sachith-seneviratne" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Sachith Seneviratne, Transport, Health and Urban Design Research Lab (THUD)</a>Cities account for more than half of the human population, and yet the quantitative analysis of the urban form remains challenging due to both the scale of analysis as well as the lack of robust labelled data. In this talk, we present efficient computer vision based deep learning workflows for effectively analyzing urban form at national/global scale. We present the use of remote sensing imagery in conjunction with self-supervised learning for various downstream tasks including infrastructure analysis, biodiversity preservation, fire-risk prediction and highlight how self-supervised techniques can enable label efficient performance on publicly available benchmarks for land use classification and change detection.Format of the session:2 x 15 min talks30 mins of discussion and question time30 mins - light morning tea

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Deep learning and data science

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Deep learning and data science

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