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Metric (mathematics)

About: Metric (mathematics) is a research topic. Over the lifetime, 42617 publications have been published within this topic receiving 836571 citations. The topic is also known as: distance function & metric.


Papers
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Proceedings ArticleDOI
15 Jun 2019
TL;DR: This paper performs linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty.
Abstract: This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. Our method achieves very competitive performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.

157 citations

Journal ArticleDOI
TL;DR: This paper provides an introductory analysis of the interference temperature metric and explains the origins, use, and challenges of an interference metric that has been proposed by the FCC.
Abstract: Interference temperature has been proposed by the FCC as a metric for interference analysis. The purpose of the metric is to demystify and remove the subjective context that has been the basis of interference analysis within the regulatory agencies. The development of an interference metric is critical if more intensive, dynamic use of the spectrum is desired. There has been very little specified as to the origins, use, and challenges of an interference metric. This paper provides an introductory analysis of the interference temperature metric.

157 citations

Journal ArticleDOI
TL;DR: The appropriateness of a given layout is computed by weighting the cost of each sequence of actions by how frequently the sequence is performed, which emphasizes frequent methods of accomplishing tasks while incorporating less frequent methods in the design.
Abstract: Numerous methods for evaluating user interfaces have been investigated to develop a metric that incorporates simple task descriptions which can assist designers in organizing their user interface. The metric, Layout Appropriateness (LA), requires a description of the sequences of actions users perform and how frequently each sequence is used. This task description can either be from observations of an existing system or from a simplified task analysis. The appropriateness of a given layout is computed by weighting the cost of each sequence of actions by how frequently the sequence is performed, which emphasizes frequent methods of accomplishing tasks while incorporating less frequent methods in the design. In addition to providing a comparison of proposed or existing layouts, an LA-optimal layout can be presented to the designer. The designer can compare the LA-optimal and existing layouts or start with the LA-optimal layout and modify it to take additional factors into consideration. >

157 citations

Posted Content
TL;DR: This work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs, and derives several practical guidelines for efficient network design, called ShuffleNet V2.
Abstract: Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs. However, the \emph{direct} metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical \emph{guidelines} for efficient network design. Accordingly, a new architecture is presented, called \emph{ShuffleNet V2}. Comprehensive ablation experiments verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff.

157 citations

Journal ArticleDOI
TL;DR: This paper introduces the 'Multiplication of Temporal Derivatives' (MTD) and demonstrates the utility of this metric to detect dynamic changes in connectivity using data from a novel state-switching simulation; accurately estimate graph structure in a previously-described 'ground-truth' simulated dataset; and identify task-driven alterations in functional connectivity.

157 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202253
20213,191
20203,141
20192,843
20182,731
20172,341