IP属地: 福建 0.1592022.02.21 16:01:36字数 871阅读 790 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》 ...
1.2019年后小样本是先验训练+元学习 2.多个正样本损失函数 3.无源码的论文直接pass掉
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)
class range:从ImageNet中随机选择500个class并将其分为400个candidate base class和100个novel class FSC setting:对于每个FSC setting,从400个candidate base class随机选择100个base class,使用所有100个novel class进行100-way 5-shot分类 image representation:在base dataset上训练ResNet18(据下文描述,应该是400个...
mini-ImageNet ... 2.3 motivation Few-shot classification中一直有这样一个问题:meta-learning 和 representation-learning 哪个表现更好。最近的文章发现仅用常规的模型做 fine-tuning,就可以达到与最好的meta-learning方法差不多的效果。因此本文探索在Few-shot classification中如何学习好的 representation。
大家好!这里介绍一下我们在ICML 2022中稿的一篇论文,主题和去年我们的NeurIPS论文一样,仍然是few-shot image classification/transfer,但这次的研究比之前更为深入,发现了特征表示的channel bias问题,可以算是挖到了当前视觉模型表示学习的一个核心问题。 一句话总结 ...
As a result, Few Shot Learning (FSL) techniques have been developed that require only a few number of samples of a class to classify a query sample of that class. In this paper, ResNet-50 and Prototypical Networks (ProNet)-based FSL approach is proposed to classify images. The proposed ...
Meta Learning Approach Based on Episodic Learning for Few-Shot Image Classification Meta-learningLearning-to-learnFew-shot learningZero-shot learningUnsupervised learningTask analysisHumans can solve image classification tasks by learning from a... SR Fatema,S Maradithaya - 《Journal of Image & Graphic...
Full size image 2.2Datasets In this section, we briefly introduce several well-known datasets for few-shot image classification. According to different data types, we categorize them into simple image dataset (Omniglot [70]), complex image dataset (MiniImageNet [120,161], TieredImageNet [122],...
In the issue of few-shot image classification, due to lack of sufficient data, directly training the model will lead to overfitting. In order to alleviate this problem, more and more methods focus on non-parametric data augmentation, which uses the information of known data to construct non-pa...