另外本文主要介绍few-shot的问题和方法,目的是为了让大家快速了解这个领域,因此并不会对方法进行详细解释,想深入了解的同学可以看:Generalizing from a few examples: a survey on few-shot learning -- PART 2。 机器学习在很多数据密集型的应用中取得了重大成功,但是当数据很少时却不太奏效。近来,Few-shot学习 ...
[论文笔记] Generalizing from a few examples: a survey on few-shot learning -- PART 2 AidenSSS 立足当下。 4 人赞同了该文章 本文紧接前文:[论文笔记] Generalizating from a few examples: a survey on few-shot learning -- PART 1 上文中,我们了解到FSL方法分为三大类:数据、模型、算法。本文中...
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参考文献 Generalizing from a Few Examples: A Survey on Few-Shot Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Learning to learn by gradient descentby gradient descent
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论文阅读:Generalizing from a Few Examples - A Survey on Few-Shot Learning 2020,发布时间:2020机器学习在数据密集型应用中取得了成功,但在数据集很少时,常常受到阻碍。符号和术语考虑学习任务T,FSL处理数据集D={Dtrain,Dtest},其中包含训练集Dtrain={(xi,yi)}Ii=
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