the number of labeled and unlabeled nodes that are connected on graphs (handled by semi-supervised node learning); 2) the few labeled nodes per label (few-shot learning); and 3) the semantical correlations among labels for they share the same subsets of nodes (multi-label classification). ...
paper: Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification source: NAACL-2022 概述: 如论文题目,小样本场景下的自动多标签提示学习,本质上就是构造简单的prompt模板,然后用预训练语言模型,对多条训练集进行模板下的掩码预测,得到多个标签词(label words),然后取平均概率最高的K个词...
Essentially, they predict the labels for unseen samples using only a few labeled samples, and have found widespread applications, such as semantic segmentation [21], multi-label node classification [46], and operational risk classification [50]. In few-shot classification, the data set consists ...
LaSO: Label-Set Operations networks for multi-label few-shot learning motivation:任务:多标签的小样本分类任务。对于两个多标签的图像,将其进行交/并/差可以得到新的图像和对应的类别,从而扩充了图像集合。 方法:在特征层面上进行样本的扩充,对于两个样本特征F1和F2,经过三个网络(M-int;M-unit;M-sub),合成...
The code here includes a training script to train new LaSO networks and test scripts for precision, image retrieval and multi-label few shot classification. Running the code Setup Create a conda environment which will automatically install necessary packages. ...
[小样本学习] 论文笔记 Learning to Self-Train for Semi-Supervised Few-Shot Classification,程序员大本营,技术文章内容聚合第一站。
解读:Few-shot classification in Named Entity Recognition Task 1 介绍 2 相关工作 3 原型网络 3.1 模型 3.2 适配NER 4 小样本实体识别 4.1 形式化任务 4.2 基本模型 4.3 实验 5 实验设置 5.1 数据集 5.2 数据准备:模拟几次实验 5.3 实验设计 5.4 模型参数 6 结果 6.1 模型的性能 6.2 ... ...
对于机器很难,这种任务就是few-shot classfication。已有的主要方法:元学习,即从一组任务中提取并传播可迁移的知识来避免过拟合,提高泛化能力。元学习方法主要分为:model initialization based methods、metric learning methods(本文所用)、hallucination based methods。
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...
In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only...