最近在VALSE Webinar 2月18日期的元学习与小样本学习的分享会上,汇报嘉宾乔思远在汇报最近few-shot的进展时提及了《Prototype Rectification for Few-Shot Learning》这篇论文。我当时发现其在one-shot上的结果有很大提升(我的阅读后所获取的理解是:文中主要针对5-way-1-shot),特意拜读了本篇文章。阅读之后还是有一...
Few-shot learningImage classificationLocal descriptorsMultiple prototypesEnd-to-end learningPrototype-based few-shot learning methods are promising in that they are simple yet effective to handle any-shot problems, and many prototype associated works are raised since then. However, these traditional ...
文章参考: 【小样本分类】Prototype Completion with Primitive Knowledge for Few-Shot Learning - 知乎 (zhihu.com)CVPR2021 |如何估计代表性的原型是少样本学习(Few-Shot Learning)的关键挑战|利用原语知识补全原型 - 知乎 (zhihu.com) Abstract 少样本学习是一个具有挑战性的任务。 在meta training之前加入一个预...
[医学图像小样本分割]A LOCATION-SENSITIVE LOCAL PROTOTYPE NETWORK FOR FEW-SHOT MEDICAL IMAGE SEGMENTATION,程序员大本营,技术文章内容聚合第一站。
Few-shot learningPrototype networkAttentionClass prototypeThe low incidence of failures and high randomness in high-speed train wheelset bearings pose significant challenges in identifying bearing defects under few-shot sample conditions. An Inception transformer (IFormer)-based weighted prototype network is...
1.什么是元学习(Meta Learning)? 元学习或者叫做“学会学习”(Learning to learn),它是要“学会如何学习”,即利用以往的知识经验来指导新任务的学习,具有学会学习的能力。由于元学习可帮助模型在少量样本下快速学习,从元学习的使用角度看,人们也称之为少次学习(Few-Shot Learning)。
PAPER{CVPR' 2021}Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning URL论文地址 CODE代码地址 1.1 Motivation# · 小样本增量学习增量类别样本过少,不足以训练好分类和蒸馏过程,不能像现有增量学习方法那样促进表示空间进一步扩展。
These problems severely limit the generalizability of such methods, necessitating further development of Few-Shot Learning (FSL). In this study, we propose the Contrastive Prototype Network (CPN) consisting of three components: (1) Contrastive learning proposed as an auxiliary path to reduce the ...
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任务: 小样本分类问题,可先在一个base数据中训练一个特征提取器和分类器,该分类器可以很好的划分特征空间中不同类别的特征。之后在fintune到N-way k-shot任务中 存在的问题描述(motivation): 在小样本分类中有两个问题:1.使用常用的特征均值的方法求类别的原型与真实的原形有偏差;2.support数据与query数据有偏差...