Metric learning
Weakly Supervised Scene Parsing with Point-Based Distance Metric Learning
... We evaluate different methods quantitatively by using pixel accuracy and mIoU which describes the the precision of pre- diction and the average performance among all classes, re- spectively. We run multiple experiments ...
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Attribute-enhanced metric learning for face retrieval
... We conduct the tests on the public dataset LFW [33] and the CFW [20] to assess the performance of the pro- posed model. The LFW dataset consists of 13,233 face images from 5749 different subjects. The CFW [20] face data ...
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Metric Learning for Dynamic Text Classification
... Prototypical Networks and Manifold Learning: This paper builds on the prototypical network ar- chitecture (Snell et al., 2017), which was origi- nally proposed in the context of few-shot learning. In both ...
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Positive Semidefinite Metric Learning Using Boosting-like Algorithms
... distance metric learning using side information that is typically presented in a set of pairwise ...tance metric by keeping training examples in equivalence constraints close, and at the same time, ...
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Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
... for learning- to-rank setting, tends to overfit to noisy examples in ...of metric learning and information re- trieval problems for which triplet loss is currently ...
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Distance Metric Learning with Eigenvalue Optimization
... to learning a Mahalanobis metric for k-nearest neigh- bor (k-NN) ...to metric learning for semi-supervised k-means ...to metric learning, we will develop novel approximate ...
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Metric Learning for Graph Based Domain Adaptation
... semi-supervised learning and supervised metric ...some metric. Instead of using predefined metric, as commonly performed, we feed the labeled instances into metric-learning ...
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
... distance metric for kNN ...by learning a linear transformation of the input space that precedes kNN classification using Euclidean ...distance metric learning as a problem in convex ...
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Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees
... The YouTube Face (YTF) (Wolf, Hassner, and Maoz 2011) contains 3,425 videos of 1,595 different persons collected from the YouTube website. In this database, there exist large variations in pose, illumination, and ...
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Distance metric learning for medical image registration
... distance metric learning can be applied to the problem of medical image ...distance metric learning algorithms we test them using images from a series of ...the learning algorithms by ...
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A review on multi-task metric learning
... Hierarchical large-scale image classification Zheng et al. [39] uses their proposed hierarchical multi-task metric learning to solve the large-scale image classification prob- lem. To deal with the ...
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Distance Metric Learning for Aspect Phrase Grouping
... Module Analysis ADDML has three modules: attention-based semantic composition module (atn), MLP-based nonlinear transformation module (mlp) and metric learning (ml). For studying the contri- bution of each ...
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Data-Adaptive Metric Learning with Scale Alignment
... existing metric learning meth- ods is to find a suitable projection matrix on the differences of all pairs of data ...“Data-Adaptive Metric Learning” (DAML), which constructs a data-adaptive ...
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Deep Metric Learning by Online Soft Mining and Class-Aware Attention
... Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data ...deep metric learn- ing is known to suffer from slow convergence due to a large fraction of ...
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Ground Metric Learning
... a metric on the features to form a distance on histograms of ...a metric between features, which is known in the computer vision literature as the ground metric, 1 is the only parameter of optimal ...
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Metric Learning for Synonym Acquisition
... apply metric learning to this ...distance metric learn- ing methods have undergone rapid development in the field of machine ...a metric such as the Mahalanobis distance can be learned from a ...
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Unsupervised Metric Learning Using Low Dimensional Embedding
... We propose a method which combines Laplacian eigenmaps and ITML to form an unsupervised metric learning method. Laplacian eigenmaps as described in 1.1 can be used to recover underlying low dimensional ...
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Metric learning with convex optimization
... Metric learning using convex optimization has attracted a lot of attention recently ...Mahalanobis metric for clustering using convex optimization to minimize the distance between examples belonging ...
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Metric Learning Tutorial
... learns a metric by using some similarity/dissimilarity information provided as a constraints. There are different formulations proposed for supervised metric learning accommodating different kinds of ...
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Learning Better Data Representation Using Inference Driven Metric Learning
... in learning a better representation of the data ...by learning the corresponding Mahalanobis distance parameter ...proposed metric learning ...
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