论文名称:Few-Shot Classification with Contrastive Learning 论文地址:[2209.08224] Few-Shot Classification with Contrastive Learning (arxiv.org) 1 Intro Thanks to the available of a large amout of annotated data, deep CNN yeild impressive results on various tasks.However, the time-consuming and costly...
为此,作者提出了container,一种新的对比学习技术,它优化了few shot NER的token间分布距离。container不是优化特定于类的属性,而是优化了一个基于高斯分布的embedding,来区分token类别的广义目标。这有效地缓解了来自训练领域的过拟合问题。作者在几个传统的测试数据集(OntoNotes, CoNLL’03, WNUT ’17, GUM)和一个新...
Few-shot contrastive learningPartial residual embedding moduleBatch compact lossInsulator identificationThis paper presents a novel discriminative Few-shot learning architecture based on batch compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably good performance in image recognition...
当目标类有多个few-shot sample(例如,5-shot)可用时,模型可以通过优化高斯embedding的KL散度有效地适应新域 相比之下,对于1-shot的情况,模型适应目标类分布的难度很大。如果模型没有关于目标类的先验知识,单个示例可能不足以推断目标类分布的方差。因此,对于one-shot场景,我们优化了 嵌入分布均值之间的平方欧氏距离。
propose a novel contrastive learning-based framework that seamlessly integrates contrastive learning into both stages to improve the performance of fewshot classification. In the pre-training stage, we propose a self-supervised contrastive loss in the forms of feature vector vs. feature map and feature...
【ECCV 2020】小样本学习论文解读 | When Does Self-supervision Improve Few-shot Learning?乐道学长 立即播放 打开App,流畅又高清100+个相关视频 更多1.5万 9 45:05 App 【ECCV 2022】小样本学习论文解读 | Few-Shot Classification with Contrastive Learning 1140 1 21:26 App 【ICCV 2019】迁移学习论文解读|...
few-shot learning涉及从很少的标记示例中学习看不见的类。为了避免对有限的可用数据进行过度拟合,引入了元学习来重点关注如何学习。提出原型网络来学习度量空间,其中特定未知类的示例围绕单个原型聚集。虽然它主要部署在计算机视觉中,但Fritzler等人和Hou等人也使用了fewshot-NER的原型网络。另一方面,Yang和Katiyar提出了一...
COVID-19 diagnosis Few-shot learning Contrastive learning Chest CT images 1. Introduction The latest coronavirus, COVID-19, was initially reported in Wuhan, China toward the end of 2019 and has since spread rapidly around the globe, leading to a worldwide crisis. As an infectious lung disease...
embeddings)的关系原型构建方法,使每个原型能够更多地关注支持文档中与关系相关的信息。然后,提出了一种实例级关系加权对比学习方法(instance-level relation weighted contrastive learning method),进一步优化了关系原型。 3.2.1 Instance-Based Prototype Construction ...
To address the challenges of scarcity in geotagged data for social user geolocation, we propose FewUser, a novel framework for Few-shot social User geolocation. We incorporate a contrastive learning strategy between users and locations to improve geolocation performance with no or limited training data...