Uncertainty-Aware Few-Shot Image Classification. 2021 1. 概述 1.1 解决问题 观测噪声使similiarity的可信度降低 由于一个少样本分类任务中,query及support图像的质量参差不齐,可能有些query和support图像明明属于同一类,但由于拍摄角度不好或物体本身比较奇特,他们的外观就是差的比较多,比如下面这两张对比图 同样是...
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? Abstract 近年来元学习研究的重点是开发能够快速适应数据有限、计算成本低的测试时间任务的学习算法。小样本学习被广泛应用于元学习的标准基准之一。在这项工作中,我们展示了一个简单的基线:在元训练集上学习一个监督或自监督表示,然后在...
这篇工作给 few-shot-classification打开了一个大门,验证了representation的重要性。此外,作者没有全盘否认meta-learning,只是说在 few-shot-classification任务中本文的方法比meta-learning好。
Few-shot image classification is the task of doing image classification with only a few examples for each category (typically < 6 examples). Source: [Learning Embedding Adaptation for Few-Shot Learning](https://github.com/Sha-Lab/FEAT)
IP属地: 香港 0.1592022.02.21 16:01:36字数 871阅读 773 Hello~ 两个月没更新啦 年都过了 虎年大吉呀大家 把剩下的一点关于小样本学习的论文阅读更新完~ 后续就是随缘更新啦 有需要交流可以简信啦 论文名称: 《few-shot image classification with multi-facet prototypes》 ...
就可以称之为few-shot classification，当运用于回归问题上时，就可以称之为few-shot regression。
Few-shot learning is widely used as one of the standard benchmarks in meta-learning. In this work, we show that a simple baseline: learning a supervised or self-supervised representation on the meta-training set, followed by training a linear classifier on top of this representation, ...
In this study, we proposed a MetaMed approach that relies on meta-learning by formulating the medical image classification for low data regime as a few-shot learning problem. We have also integrated advanced augmentation techniques like CutOut, MixUp, and CutMix to regularize the model, and its...
Improved Few-Shot Visual Classification草草阅读 Simple CNAPS 文章来源:CVPR2020 文章地址:https://arxiv.org/pdf/1912.03432.pdf 1、contribution: 文章采用了Mahalanobis距离进行分类,可改进非线性分类器的决策边界,似乎是用在了embedding空间中,此前的观点认为在embedding空间中使用什么度量标准没有那么重要,但是此...