A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges
Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …
the data, which enhances the performance of similarity-based algorithms. This tutorial …
Distilled representation using patch-based local-to-global similarity strategy for visual place recognition
Abstract Visual Place Recognition (VPR) is important for ensuring the accuracy and
reliability of re-localization in a Visual Simultaneous Localization and Mapping (VSLAM) …
reliability of re-localization in a Visual Simultaneous Localization and Mapping (VSLAM) …
Kernel-Based Distance Metric Learning for Supervised -Means Clustering
B Nguyen, B De Baets - IEEE transactions on neural networks …, 2019 - ieeexplore.ieee.org
Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …
examples is a key to the success of k-means clustering. While it is not always an easy task to …
Ship classification in SAR images with geometric transfer metric learning
Y Xu, H Lang - IEEE Transactions on Geoscience and Remote …, 2020 - ieeexplore.ieee.org
There are still many challenges to be resolved in the task of ship classification in synthetic
aperture radar (SAR) images, such as limited number of labeled samples in SAR domain …
aperture radar (SAR) images, such as limited number of labeled samples in SAR domain …
Geometric order learning for rank estimation
A novel approach to rank estimation, called geometric order learning (GOL), is proposed in
this paper. First, we construct an embedding space, in which the direction and distance …
this paper. First, we construct an embedding space, in which the direction and distance …
A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges (with appendices on mathematical …
Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …
the data, which enhances the performance of similarity-based algorithms. This tutorial …
Few-shot contrastive learning for image classification and its application to insulator identification
L Li, W Jin, Y Huang - Applied Intelligence, 2022 - Springer
This paper presents a novel discriminative Few-shot learning architecture based on batch
compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably …
compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably …
pydml: A python library for distance metric learning
pyDML is an open-source python library that provides a wide range of distance metric
learning algorithms. Distance metric learning can be useful to improve similarity learning …
learning algorithms. Distance metric learning can be useful to improve similarity learning …
Ordinal regression with explainable distance metric learning based on ordered sequences
The purpose of this paper is to introduce a new distance metric learning algorithm for ordinal
regression. Ordinal regression addresses the problem of predicting classes for which there …
regression. Ordinal regression addresses the problem of predicting classes for which there …
Distribution shift metric learning for fine-grained ship classification in SAR images
Y Xu, H Lang - IEEE Journal of Selected Topics in Applied …, 2020 - ieeexplore.ieee.org
Fine-grained ship classification in synthetic aperture radar (SAR) images is a challenging
task, since SAR images can only provide limited discriminative information due to the …
task, since SAR images can only provide limited discriminative information due to the …