an algorithm for meta-learning that is model-agnostic, in the sense: it is compatible(adj.相容的) with any model trained with gradient descent and applicable to a variety of different learning task. the goal of meta-learning: train a model on a variety of learning tasks, such that it can...
meta learning参考:里面提到了 minist 数据集上的meta-learning的使用,一共是 20 个种类的字母,选 N 类字母,每个字母抽取 K 个样本,构成训练集,同样的方法构成测试集,即 N-classes K-shot。(我觉得上面的 N 类就是算法里的各个\mathcal{T}_i,这样就明白了!但是关于任务的分布p(\mathcal{T})又是什么鬼...
在meta learning中我们不仅要寻找对所有任务都最优的初始化参数,当遇到新任务的时候也要可以微调模型适应新的任务(we will not simply use the data from other tasks to find parameters that are optimal for all tasks, but keep the option tofine-tuneour model)。因此优化目标就可以写作: 其中, 是一个将 ...
在我们 meta-learning 场景下,我们考虑一个基于任务的分布。 2. A Model-Agnostic Meta-Learning Algorithm: 与前人的方法不同,我们提出一种方法,可以学习任何标准模型的参数,通过 meta-learning,使该模型可以准备好进行 fast adaptation。这个方法的直观原因是:some internal representations are more transferrable than...
Model-Agnostic Meta-Learning (MAML) 理解 模型不可知元学习(Model-Agnostic Meta-Learning, MAML)的目标是使模型每次的梯度更新更有效、提升模型的学习效率、泛化能力等,它可以被看做一种对模型进行预训练的方法,适用于小样本学习。 看论文中Algorithm 2,在有监督分类任务上进行理解。为了表达更清晰,下面的...
2.2. A Model-Agnostic Meta-Learning Algorithm 与先前的研究相反,后者试图训练可吸收整个数据集的RNN (Santoro et al., 2016; Duan et al., 2016b)或可以在测试时与非参数化方法结合的特征嵌入(Vinyals et al., 2016; Koch, 2015),我们提出了一种方法,该方法可以通过元学习来学习任何标准模型的参数,从而为...
MAML学习资料推荐 Model-AgnosticMeta-Learning for Fast Adaptation of DeepNetworks,程序员大本营,技术文章内容聚合第一站。
PMLR 2017 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,程序员大本营,技术文章内容聚合第一站。
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-lear...
2.2. A Model-Agnostic Meta-Learning Algorithm 与之前的研究相反,之前的研究试图训练能摄取整个数据集的递归神经网络(Santoro et al., 2016;Duan et al.,2016b)或特征嵌入,在测试时可与非参数方法结合(Vinyals et al.,2016;(Koch, 2015),我们提出了一种方法,可以通过元学习学习任何标准模型的参数,从而为模型...