Understanding and Exploiting Dependent Variables with Deep Metric Learning
Deep Metric Learning (DML) approaches learn to represent inputs to a lower-dimensional latent space such that the distance between representations in this space corresponds with a predefined notion of similarity. This paper investigates how the mapping element of DML may be exploited in situations w...
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ftdatacite:10.48550/arxiv.2009.03820 2023-05-15T16:01:14+02:00 Understanding and Exploiting Dependent Variables with Deep Metric Learning Mahony, Niall O' Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Velasco-Hernandez, Gustavo Riordan, Daniel Walsh, Joseph 2020 https://dx.doi.org/10.48550/arxiv.2009.03820 https://arxiv.org/abs/2009.03820 unknown arXiv https://dx.doi.org/10.1007/978-3-030-55180-3_8 Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 CC-BY-SA Computer Vision and Pattern Recognition cs.CV Artificial Intelligence cs.AI FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2020 ftdatacite https://doi.org/10.48550/arxiv.2009.03820 https://doi.org/10.1007/978-3-030-55180-3_8 2022-03-10T15:03:37Z Deep Metric Learning (DML) approaches learn to represent inputs to a lower-dimensional latent space such that the distance between representations in this space corresponds with a predefined notion of similarity. This paper investigates how the mapping element of DML may be exploited in situations where the salient features in arbitrary classification problems vary over time or due to changing underlying variables. Examples of such variable features include seasonal and time-of-day variations in outdoor scenes in place recognition tasks for autonomous navigation and age/gender variations in human/animal subjects in classification tasks for medical/ethological studies. Through the use of visualisation tools for observing the distribution of DML representations per each query variable for which prior information is available, the influence of each variable on the classification task may be better understood. Based on these relationships, prior information on these salient background variables may be exploited at the inference stage of the DML approach by using a clustering algorithm to improve classification performance. This research proposes such a methodology establishing the saliency of query background variables and formulating clustering algorithms for better separating latent-space representations at run-time. The paper also discusses online management strategies to preserve the quality and diversity of data and the representation of each class in the gallery of embeddings in the DML approach. We also discuss latent works towards understanding the relevance of underlying/multiple variables with DML. Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) The Gallery ENVELOPE(-86.417,-86.417,72.535,72.535) |
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collection |
DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
Computer Vision and Pattern Recognition cs.CV Artificial Intelligence cs.AI FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV Artificial Intelligence cs.AI FOS Computer and information sciences Mahony, Niall O' Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Velasco-Hernandez, Gustavo Riordan, Daniel Walsh, Joseph Understanding and Exploiting Dependent Variables with Deep Metric Learning |
topic_facet |
Computer Vision and Pattern Recognition cs.CV Artificial Intelligence cs.AI FOS Computer and information sciences |
description |
Deep Metric Learning (DML) approaches learn to represent inputs to a lower-dimensional latent space such that the distance between representations in this space corresponds with a predefined notion of similarity. This paper investigates how the mapping element of DML may be exploited in situations where the salient features in arbitrary classification problems vary over time or due to changing underlying variables. Examples of such variable features include seasonal and time-of-day variations in outdoor scenes in place recognition tasks for autonomous navigation and age/gender variations in human/animal subjects in classification tasks for medical/ethological studies. Through the use of visualisation tools for observing the distribution of DML representations per each query variable for which prior information is available, the influence of each variable on the classification task may be better understood. Based on these relationships, prior information on these salient background variables may be exploited at the inference stage of the DML approach by using a clustering algorithm to improve classification performance. This research proposes such a methodology establishing the saliency of query background variables and formulating clustering algorithms for better separating latent-space representations at run-time. The paper also discusses online management strategies to preserve the quality and diversity of data and the representation of each class in the gallery of embeddings in the DML approach. We also discuss latent works towards understanding the relevance of underlying/multiple variables with DML. |
format |
Article in Journal/Newspaper |
author |
Mahony, Niall O' Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Velasco-Hernandez, Gustavo Riordan, Daniel Walsh, Joseph |
author_facet |
Mahony, Niall O' Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Velasco-Hernandez, Gustavo Riordan, Daniel Walsh, Joseph |
author_sort |
Mahony, Niall O' |
title |
Understanding and Exploiting Dependent Variables with Deep Metric Learning |
title_short |
Understanding and Exploiting Dependent Variables with Deep Metric Learning |
title_full |
Understanding and Exploiting Dependent Variables with Deep Metric Learning |
title_fullStr |
Understanding and Exploiting Dependent Variables with Deep Metric Learning |
title_full_unstemmed |
Understanding and Exploiting Dependent Variables with Deep Metric Learning |
title_sort |
understanding and exploiting dependent variables with deep metric learning |
publisher |
arXiv |
publishDate |
2020 |
url |
https://dx.doi.org/10.48550/arxiv.2009.03820 https://arxiv.org/abs/2009.03820 |
long_lat |
ENVELOPE(-86.417,-86.417,72.535,72.535) |
geographic |
The Gallery |
geographic_facet |
The Gallery |
genre |
DML |
genre_facet |
DML |
op_relation |
https://dx.doi.org/10.1007/978-3-030-55180-3_8 |
op_rights |
Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 |
op_rightsnorm |
CC-BY-SA |
op_doi |
https://doi.org/10.48550/arxiv.2009.03820 https://doi.org/10.1007/978-3-030-55180-3_8 |
_version_ |
1766397183675858944 |