Deep Metric Learning-based Semi-supervised Regression With Alternate Learning

Abstract

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 th

Stats

• citations4
• S2 citations—
• github stars0
• HF likes0
• heat score5.24
• arxiv keyzell2022deep

Related papers

• Metric Learning By Similarity Network For Deep Semi-supervised Learning (2020)3.58
• S2SD: Simultaneous Similarity-based Self-distillation For Deep Metric Learning (2020)3.31
• Semi-supervised Deep Learning By Metric Embedding (2016)0.00
• Guided Deep Metric Learning (2022)6.77
• Adaptive Hierarchical Similarity Metric Learning With Noisy Labels (2021)10.74
• Deep Metric Learning Assisted By Intra-variance In A Semi-supervised View Of Learning (2023)5.24
• A New Similarity Space Tailored For Supervised Deep Metric Learning (2020)3.58
• Cross-modal Deep Metric Learning With Multi-task Regularization (2017)9.03