Computer Science > Machine Learning
arXiv:2204.13572 (cs)
[Submitted on 28 Apr 2022 (v1), last revised 27 Aug 2022 (this version, v3)]
Title:Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Authors:Luiz H. Buris,Daniel C. G. Pedronette,Joao P. Papa,Jurandy Almeida,Gustavo Carneiro,Fabio A. Faria
View a PDF of the paper titled Mixup-based Deep Metric Learning Approaches for Incomplete Supervision, by Luiz H. Buris and 5 other authors
View PDFAbstract:Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real applications becomes challenging due to the large labeled collections required during training. Several works have pursued solutions to overcome it by proposing strategies that can learn more for less, e.g., weakly and semi-supervised learning approaches. As these approaches do not usually address memorization and sensitivity to adversarial examples, this paper presents three deep metric learning approaches combined with Mixup for incomplete-supervision scenarios. We show that some state-of-the-art approaches in metric learning might not work well in such scenarios. Moreover, the proposed approaches outperform most of them in different datasets.
| Comments: | 5 pages, 1 figure, accepted for presentation at the ICIP2022 |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2204.13572 [cs.LG] |
| (orarXiv:2204.13572v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2204.13572 arXiv-issued DOI via DataCite |
Submission history
From: Fabio Augusto Faria [view email][v1] Thu, 28 Apr 2022 15:36:16 UTC (3,359 KB)
[v2] Tue, 23 Aug 2022 17:52:51 UTC (3,368 KB)
[v3] Sat, 27 Aug 2022 13:41:35 UTC (11,195 KB)
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View a PDF of the paper titled Mixup-based Deep Metric Learning Approaches for Incomplete Supervision, by Luiz H. Buris and 5 other authors
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