Abstract
Deep Metric learning (DML) is gaining popularity recently as a way of exploiting the advantages of deep learning in applications where the task involves adaption to variable object features, e.g. facial verification, person re-identification. In applications of deep learning where generalisability is difficult to achieve, DML provides an architecture which has the facility for the algorithm’s output’s to be adapted to each use case by framing classification tasks as a reidentification problem. At the inference stage, query embeddings generated by the DML model are compared against a gallery of embeddings in a latent space. This paper will investigate online database management strategies to preserve the quality and diversity of data and the representation of each class in the gallery of embeddings. We propose the use of SQL to facilitate the removal of outliers from an embedding database and also discuss latent works which study the geometric and statistical relationships between embeddings to formulate methods for outlier embeddings removal.
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Acknowledgments
This work was supported, in part, by Science Foundation Ireland grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie). The authors wish to acknowledge the DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support.
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Mahony, N.O., Campbell, S., Carvalho, A., Krpalkova, L., Riordan, D., Walsh, J. (2022). Improving Accuracy and Latency in Image Re-identification by Gallery Database Cleansing. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_60
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DOI: https://doi.org/10.1007/978-3-030-80119-9_60
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