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...
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 主要思想: 针对第一个问题,作者采用自监督的方法在support数据中增加额外的无标签的数据,首先用旧的分类器对每个无标签的数据...
Prototype rectificationIntra-class biasCross-class biasFew-shot learning requires to recognize novel classes with scarce labeled data. Prototypical network is useful in existing researches, however, training on narrow-size distribution of scarce data......
Prototype Rectification for Few-shot Learning 文献阅读 本文是我对《Prototype Rectification for Few-shot Learning》一文的理解,难免有不足之处,欢迎大家多多交流,批评指正~ 算法框图 算法步骤 文章主要贡献 理论推导 实验结果 总结 本文是我对《Prototype Rectification for Few-... 查看原文 多域test时unseen...
Zero-shot Learning / One-shot Learning / Few-shot Learning Zero-shotLearning/ One-shotLearning/Few-shotLearning。 爱上一匹野马 (泛化能力),可我的家里没有草原 (海量数据) 。Learning类型 分为...。Few-shotLearningFew-shotLearning,少量学习。 也即 One-shotLearning。 传统Learning即传统深度学习的海量...
For few-shot learning tasks, you need to set base2new to False in main.py and modify backbone to RN50 in yaml to run code in the following format: python main.py --config ./configs/fgvc.yaml --shots 1 --model CPR --subsample all For few-shot learning tasks, you need to set bas...
Few-shot learningTask embeddingPrototype rectificationMetric learningIn realistic scenarios, few-shot classification aims to generalize from common classes to novel classes with limited labeled samples. Most of existing transductive methods concentrate on probing into instance-prototype relations in a fixed ...
Interaction Information Guided Prototype Representation Rectification for Few-Shot Relation Extractiondoi:10.3390/electronics12132912PROTOTYPESINFORMATION networksFew-shot relation extraction aims to identify and extract semantic relations between entity pairs using only a small number of annotated...
受self-attention of transformer启发,我们将支持集和查询集作为一个整体,利用全局上下文通过自注意机制来学习与任务相关的特征。然后,我们利用学习到的任务相关特征来计算类原型,并预测每个查询样本的伪标签和置信度。我们利用高置信度的查询样本扩大了支持集,并对类原型进行了修正,希望学习到的类原型能够更好地突出类...
Prototype Rectification for Few-Shot Learning 有关少样本学习(Few-shot Learning)的研究论文《Prototype Rectification for Few-Shot Learning》被全球计算机视觉顶会ECCV 2020接收为Oral论文(入选率仅2%)。 ECCV全称为European Conference on Computer Vision(欧洲计算机视觉国际会议),与ICCV和CVPR合称为全球计算机视觉...