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...
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...
从已有方法可以看出，NLP解决Few-Shot Learning问题的有效方法就是，引入大规模外部知识或数据，因此无标注...
【9】Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces.【10】Few-Shot Text...
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one cl...
Transductive multi-view zero-shot learning (TPAMI 2015) Label propagation: Learning with local and global consistency (NIPS 2004) 如何解决label propagation中的variance parameter: Label propagation through linear neighborhoods (ICML 2006) Learning from labeled and unlabeled data with label propagation (CMU...
【论文解读 AAAI 2021】Few-shot Learning for Multi-label Intent Detection,程序员大本营,技术文章内容聚合第一站。
Generating ClassificationWeights with GNN Denoising Autoencoders fo Few-Shot Learning (oral) motivation:利用去噪自编码器的思想,并考虑类间的关系,生成分类器的权重。由于小样本得到的分类器具有较大的噪声,所以可以采用去噪编码器对其精化。 方法: 利用所有的训练数据预训练一个特征提取器,并得到所有训练分类层的...
Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel unseen classes, each represented by few labeled examples. In such a scenario, pretraining a network with high capacity on the large dataset and then finetuning it on the few examples ...
multi-label text classification A new relation classification dataset called FewRel 在FSL的Theories方面: 目前仍有许多理论问题有待探索,对FSL算法的收敛性还没有完全了解。特别是,元学习方法在任务分布上优化θ,而不是在单个任务上。 这篇论文篇幅很长,也看了很长时间,觉得meta-learning在FSL中扮演着重要的角色...