Deep Metric Learning-Based Semi-Supervised Regression with Alternate Learning ...

• Main Authors: Zell, Adina, Sumbul, Gencer, Demir, Begum
• Format: Conference Object
• Language: English
• Published: IEEE 2022
• Subjects: 000 Informatik, Informationswissenschaft, allgemeine Werke000 Informatik, Wissen, Systeme004 Datenverarbeitung; Informatik; semi-supervised regression; parameter estimation; metric learning; deep learning; DML
• Online Access: https://dx.doi.org/10.14279/depositonce-19433; https://depositonce.tu-berlin.de/handle/11303/20635

This paper introduces a novel deep metric learning-based semi-supervised regression (DML-S2R) method for parameter estimation problems. The proposed DML-S2R method aims to mitigate the problems of insufficient amount of labeled samples without collecting any additional sample with a target value. To this end, it is made up of two main steps: i) pairwise similarity modeling with scarce labeled data; and ii) triplet-based metric learning with abundant unlabeled data. The first step aims to model pairwise sample similarities by using a small number of labeled samples. This is achieved by estimating the target value differences of labeled samples with a Siamese neural network (SNN). The second step aims to learn a triplet-based metric space (in which similar samples are close to each other and dissimilar samples are far apart from each other) when the number of labeled samples is insufficient. This is achieved by employing the SNN of the first step for triplet-based deep metric learning that exploits not only ...