Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification

In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity ba...

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Autores principales:	Youngjae Lee, Hyeyoung Park
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	few-shot classification metric-learning probabilistic similarity intra-class statistics Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7f50fb27736747cb881c6b3232758cdb
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id	oai:doaj.org-article:7f50fb27736747cb881c6b3232758cdb
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spelling	oai:doaj.org-article:7f50fb27736747cb881c6b3232758cdb2021-11-25T16:42:18ZEffect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification10.3390/app1122109772076-3417https://doaj.org/article/7f50fb27736747cb881c6b3232758cdb2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10977https://doaj.org/toc/2076-3417In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply the probabilistic similarity to obtain loss value for episodic learning of embedding network as well as to classify unseen test data. By defining the probabilistic similarity as the probability density of difference vectors between two samples with the same class label, it is possible to obtain a more reliable estimate of the similarity especially for the case of large number of classes. Through experiments on various benchmark data, we confirm that the probabilistic similarity can improve the classification performance, especially when the number of classes is large.Youngjae LeeHyeyoung ParkMDPI AGarticlefew-shot classificationmetric-learningprobabilistic similarityintra-class statisticsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10977, p 10977 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	few-shot classification metric-learning probabilistic similarity intra-class statistics Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	few-shot classification metric-learning probabilistic similarity intra-class statistics Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Youngjae Lee Hyeyoung Park Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
description	In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply the probabilistic similarity to obtain loss value for episodic learning of embedding network as well as to classify unseen test data. By defining the probabilistic similarity as the probability density of difference vectors between two samples with the same class label, it is possible to obtain a more reliable estimate of the similarity especially for the case of large number of classes. Through experiments on various benchmark data, we confirm that the probabilistic similarity can improve the classification performance, especially when the number of classes is large.
format	article
author	Youngjae Lee Hyeyoung Park
author_facet	Youngjae Lee Hyeyoung Park
author_sort	Youngjae Lee
title	Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
title_short	Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
title_full	Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
title_fullStr	Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
title_full_unstemmed	Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
title_sort	effect of probabilistic similarity measure on metric-based few-shot classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7f50fb27736747cb881c6b3232758cdb
work_keys_str_mv	AT youngjaelee effectofprobabilisticsimilaritymeasureonmetricbasedfewshotclassification AT hyeyoungpark effectofprobabilisticsimilaritymeasureonmetricbasedfewshotclassification
_version_	1718413028206051328

Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification

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