meta-transfer-learning:TensorFlow和PyTorch实施“少量学习的元传输学习”(CVPR2019)

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元转移学习,少量学习 该存储库包含针对论文的TensorFlow和PyTorch实现,作者孙倩*,*,( )和( (* =相等贡献)。 如果您对此存储库或相关文章有任何疑问,请随时或。 检查快照分类排行榜。 概括 介绍 入门 数据集 表现 引文 致谢 介绍 已经提出将元学习作为解决具有挑战性的一次性学习设置的框架。 关键思想是利用大量类似的少量任务,以学习如何使基础学习者适应新的任务,对于该新任务,只有少量标记的样本可用。 由于深度神经网络(DNN)仅仅使用少数几个样本就趋于过拟合,因此元学习通常使用浅层神经网络(SNN),因此限制了其有效性。 在本文中,我们提出了一种称为元转移学习(MTL)的新颖的少拍学习方法,该方法可以学习将深度神经网络适应于少拍学习任务。 具体来说,meta是指训练多个任务,并且通过学习每个任务的DNN权重的缩放和移位功能来实现传递。 我们在两个具有挑

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