论文标题
元学习需要荟萃提取
Meta-Learning Requires Meta-Augmentation
论文作者
论文摘要
元学习算法旨在学习两个组件：一个预测任务目标的模型，以及一个基础学习者，在从新任务中给出示例时会快速更新该模型。这种额外的学习水平可以很强大，但它也创造了另一个潜在的过度拟合来源，因为我们现在可以在模型或基础学习者中过度拟合。我们描述了这两种形式的金属性过度拟合，并证明它们以实验性出现在常见的元学习基准中。然后，我们使用信息理论框架来讨论元夸大，这是一种添加随机性的方式，从而使基础学习者和模型从学习不推广到新任务的琐碎解决方案中。我们证明，元夸大为最近提出的元元素化技术带来了很大的互补益处。
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new task. This additional level of learning can be powerful, but it also creates another potential source for overfitting, since we can now overfit in either the model or the base learner. We describe both of these forms of metalearning overfitting, and demonstrate that they appear experimentally in common meta-learning benchmarks. We then use an information-theoretic framework to discuss meta-augmentation, a way to add randomness that discourages the base learner and model from learning trivial solutions that do not generalize to new tasks. We demonstrate that meta-augmentation produces large complementary benefits to recently proposed meta-regularization techniques.
