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...
Few shot Learnig是解决小样本问题的,我们希望机器学习模型通过学习一定类别的大量数据之后,对于新的类别,只需要很少量的样本就可以快速学习,Few shot Learning是Meta Learning在监督学习领域的应用。Few shot Learning的训练集中包含了很多的类别,每个类别又包括很多样本,在训练阶段从训练集中随机抽取C个类别,每个类别抽...
(2)最近元学习(meta-learning)被用来解决小样本学习的问题(few-shot problem),元学习模型通常包含两个部分,分别是初始模型(initial model)和可以在少量新的任务上进行训练的更新策略(updating strategy)。元学习的目标是自动地meta-learn更新两个部分的参数以在新的一系列task上实现泛化能力; (3)元学习现阶段的一个...
元学习已经被few-shot learning 大量运用,key idea 是利用大量相似的few-shot 任务从而获得一个base-learner which 可以被应用到只有少量监督样本的任务中。在只有少量样本的情况下DNN容易过拟合,因此元学习通常使用SNNs(Shallow neural networks),同时effectiveness 受到限制。本文提出了一个新的算法MTL(meta-transfer le...
Meta-learningFew-shot learningMeta-learning is an effective tool to address the few-shot learning problem, which requires new data to be classified considering only a few training examples. However, when used for classification, it requires large labeled datasets, which are not always available in...
Meta learning通过构建meta task用来训练一个base learner,使其在新任务上能够快速适应,如MAML,作者指出,MAML的方式,需要大量的meta task(240k),效率太低,而且只work在比较浅层的神经网络(4 conv)。 解决这两个问题,也是文章的主要贡献点。 Few shot learning可以大致划分成三个方向:metric learning method,memory...
Few shot Learnig是解决小样本问题的,我们希望机器学习模型通过学习一定类别的大量数据之后,对于新的类别,只需要很少量的样本就可以快速学习,Few shot Learning是Meta Learning在监督学习领域的应用。Few shot Learning的训练集中包含了很多的类别,每个类别又包括很多样本,在训练阶段从训练集中随机抽取C个类别,每个类别抽...
MetaLearning＆Few-shotLearning（元学习VS⼩样本学习）Meta Learning ＆ Few-shot Learning(元学习VS⼩样本学习)⼀、Meta Learning：元学习，learn to learn 让机器学会如何学习，即让机器学习⾃⼰调整模型中的超参数。 在传统机器学习中，输⼊的是样本，损失函数是在训练集数据上得到的。主要⼯作...
Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is ...
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 ...