Stochastic class-based hard example mining for deep metric learning
Performance of deep metric learning depends heavily on the capability of mining hard
negative examples during training. However, many metric learning algorithms often require …
negative examples during training. However, many metric learning algorithms often require …
Spectral, probabilistic, and deep metric learning: Tutorial and survey
This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …
probabilistic, and deep metric learning. We first start with the definition of distance metric …
Semi-supervised multi-view deep discriminant representation learning
Learning an expressive representation from multi-view data is a key step in various real-
world applications. In this paper, we propose a semi-supervised multi-view deep …
world applications. In this paper, we propose a semi-supervised multi-view deep …
Primitivenet: Primitive instance segmentation with local primitive embedding under adversarial metric
We present PrimitiveNet, a novel approach for high-resolution primitive instance
segmentation from point clouds on a large scale. Our key idea is to transform the global …
segmentation from point clouds on a large scale. Our key idea is to transform the global …
Linearity-aware subspace clustering
Obtaining a good similarity matrix is extremely important in subspace clustering. Current
state-of-the-art methods learn the similarity matrix through self-expressive strategy …
state-of-the-art methods learn the similarity matrix through self-expressive strategy …
Curvilinear distance metric learning
Abstract Distance Metric Learning aims to learn an appropriate metric that faithfully
measures the distance between two data points. Traditional metric learning methods usually …
measures the distance between two data points. Traditional metric learning methods usually …
Unsupervised deep metric learning via orthogonality based probabilistic loss
Metric learning is an important problem in machine learning. It aims to group similar
observations together. Existing state-of-the-art metric learning approaches require class …
observations together. Existing state-of-the-art metric learning approaches require class …
Adversarial similarity metric learning for kinship verification
Z Wei, M Xu, L Geng, H Liu, H Yin - IEEE Access, 2019 - ieeexplore.ieee.org
Given a pair of facial images, it is an interesting yet challenging problem to determine if there
is a kin relation between them. Recent research on that topic has made encouraging …
is a kin relation between them. Recent research on that topic has made encouraging …
On the robustness of metric learning: an adversarial perspective
Metric learning aims at automatically learning a distance metric from data so that the precise
similarity between data instances can be faithfully reflected, and its importance has long …
similarity between data instances can be faithfully reflected, and its importance has long …
Time-frequency deep metric learning for multivariate time series classification
Z Chen, Y Liu, J Zhu, Y Zhang, R **, X He, J Tao… - Neurocomputing, 2021 - Elsevier
Multivariate time series (MTS) data exist in various fields of studies and MTS classification is
an important research topic in the machine learning community. Researchers have …
an important research topic in the machine learning community. Researchers have …