The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures.
Abstract—Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and ...
The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures.
Mar 28, 2024 · We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ...
Missing: Dynamic | Show results with:Dynamic
Compared with previous metric learning researches, the Dynamic Metric Learning lays emphasis on the capacity to simultaneously accommodate multiple semantic ...
Missing: Pairwise | Show results with:Pairwise
Aug 27, 2018 · This method is formulated as a pairwise constrained optimization problem. In each training cycle, this method selects the nearest similar and ...
Kristjan H. Greenewald, Stephen Kelley, Alfred O. Hero III: Dynamic Metric Learning from Pairwise Comparisons. CoRR abs/1610.03090 (2016).
We propose a simple and effective distance metric learning method that leverages differentiable decision trees and pairwise metric learning to induce strong ...
In each training cycle, this method selects the nearest similar and dissimilar neighbors of each sample to construct the pairwise constraints, and then solve ...
[PDF] Dynamic Metric Learning with Cross-Level Concept Distillation
www.ecva.net › eccv_2022 › papers
We compare our CLCD with all the methods provided by the dynamic metric learning benchmark [47] as shown in Table 1, including the cross-level deep metric ...
Missing: Pairwise | Show results with:Pairwise