今天给大家介绍新加坡国立大学的Qianru Sun等人在2019年CVPR(计算机视觉顶会)上发表的文章“Meta-Transfer Learning for Few-ShotLearning”。本文对MAML(Model-Agnostic Meta-Learning)进行了一些改进,提出了meta-transfer learning(MTL)(可以整合迁移学习和元学习的优点)仅用少量数据就能快速适应新的task。 一、摘要 元...
在cifar-100数据集上,全监督学习可以实现75.7%的准确率,而1-shot learning 仅仅可以实现40.1%的准确率。 Few-shot learning methods 可以被简单的分类为两部分,数据扩充和基于任务的meta-learning。数据扩充是指增加可用数据的数量,并且对FSL 是useful。第一种是数据生成的方式,如利用高斯噪声,但是这种方式在few-sho...
While humans tend to be highly effective in this context, often grasping the essential connection between new concepts and their own knowledge and experience, it remains challenging for machine learning approaches. 解决问题: 一个深的卷积网络 for few shot learning 具体的对应MAML问题 1 网络层数浅,...
Transfer learningFew-shot learningReptileInertial sensorHuman activity recognitionDeep learning has proven to be highly effective for human activity recognition (HAR) when large amount of labelled data is available for the target task. However, training a deep learning model to generalize well on a ...
论文首先指出meta-learning存在的一大问题是数据batch设置问题, meta-learning在few-shot learning场景下用的是n-way k-shot 数据格式 也就是 n个类别的图片 每个具有k个带标签样本作为一个最小的训练单位(后面也称为task), 这样的设置相对于transfer来说meta-learning给batch大小带来了一个下界, 因为半个task数据...
Supervised learning with the restriction of a few existing training samples is called Few-Shot Learning. FSL is a subarea that puts deep learning performance in a gap, as building robust deep networks requires big training data. Using transfer learning i
The advent of Deep Learning algorithms for mobile devices and sensors has ushered in an unprecedented explosion in the availability and number of systems trained on a wide range of machine learning tasks, allowing for many new opportunities and challenges in the realm of transfer learning. Currently...
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As ...
3.1.2.1Few-shot learning-based transfer learning models Few-shot learning (FSL), also known as low-shot learning (LSL), is a machine learning problem in which the training dataset contains just a tiny amount of data. This has been introduced to address the issue of domain adaptation with a...
Firstly, we use dependency-based word embedding models as background spaces for few-shot learning. Secondly, we introduce two few-shot learning methods ... S Preda,G Emerson 被引量: 0发表: 2022年 Memory, Show the Way: Memory Based Few Shot Word Representation Learning Distributional semantic ...