Negative sampling for triplet-based loss: improving representation in self-supervised representation learning

Manuel Alejandro Goyo; Hidalgo Barrientos, Mauricio Fernando

Negative sampling for triplet-based loss: improving representation in self-supervised representation learning

dc.contributor.author	Manuel Alejandro Goyo
dc.contributor.author	Hidalgo Barrientos, Mauricio Fernando
dc.date.accessioned	2024-12-03T13:06:57Z
dc.date.available	2024-12-03T13:06:57Z
dc.date.issued	2024-11-17
dc.description.abstract	Significant strides have been made in artificial neural networks across various fields, necessitating extensive labeled data for effective training. However, the acquisition of such annotated data is both costly and labor-intensive. To address this challenge, Self-Supervised Representation Learning (SSRL) has emerged as a promising solution. One prominent SSRL method, Contrastive Self-Supervised Learning (CSL), enhances feature representations by discerning similarities and differences among samples in the feature space. Yet, accurately identifying dissimilar samples remains a persistent issue, limiting CSL’s effectiveness. In response, an innovative enhancement to CSL is proposed in this paper. Explicit negative sampling strategies using a binary classification algorithm within the feature space are introduced to distinguish between similar and dissimilar features precisely. Additionally, Triplet Loss, originally designed for tasks such as person re-identification and face recognition, is incorporated to further refine feature learning. Experimental evaluations on the CIFAR-10 and SVHN datasets validate the proposed method’s superiority in content-based image retrieval (CBIR) and classification tasks. Significant improvements are demonstrated in metrics such as mean average precision (MAP), accuracy, recall, precision, and F1-score compared to existing techniques. This framework contributes to the advancement of SSRL by enabling scalable neural network training on large datasets with minimal annotation, effectively bridging the gap between supervised and unsupervised learning paradigms.
dc.identifier.citation	En: Hernández-García, Ruben; Barrientos, Ricardo J.; Velastin, Sergio A. (eds). Progress in pattern recognition, image analysis, computer vision, and applications: 27th Iberoamerican Congress, CIARP 2024 Talca, Chile. November 26-29, 2024, Proceeding, Part I. Springer, 2025. pp. 133-147
dc.identifier.doi	https://doi.org/10.1007/978-3-031-76607-7_10
dc.identifier.isbn	9783031766060
dc.identifier.isbn	9783031766077
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.orcid	https://orcid.org/0000-0003-3191-3673
dc.identifier.uri	http://hdl.handle.net/20.500.12254/3955
dc.language.iso	en
dc.publisher	Springer
dc.relation.ispartof	Lecture Notes in Computer Science
dc.relation.ispartof	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
dc.relation.ispartofseries	Lecture Notes in Computer Science; 15368
dc.rights	Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subject	Self representation learning
dc.subject	Self supervision
dc.subject	Triplet loss
dc.subject	Negative sampling
dc.subject	Image classification
dc.subject	Computer vision
dc.title	Negative sampling for triplet-based loss: improving representation in self-supervised representation learning
dc.type	book-chapter