论文标题
一个嵌套的双级优化框架，可用于强大的几次射击学习
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
论文作者
论文摘要
基于流行的基于梯度的元学习框架，模型 - 不合时宜的元学习（MAML）假设每个任务或实例对元学习者的贡献是相等的。因此，它无法在几次学习中解决基础和新颖类之间的领域变化。在这项工作中，我们提出了一种新颖的强大元学习算法NestedMaml，该算法学会了为培训任务或实例分配权重。我们将权重视为超参数，并使用在嵌套的双层优化方法中设置的一组验证任务（与MAML中的标准BI级优化相反）。然后，我们在元训练阶段应用NestedMAML，涉及（1）从不同于元测试任务分布的分布中采样的几个任务，或（2）一些带有嘈杂标签的数据样本。关于合成和现实世界数据集的广泛实验表明，NestedMaml有效地减轻了“不必要的”任务或实例的影响，从而导致对最先进的强大元学习方法的显着改善。
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, it fails to address the domain shift between base and novel classes in few-shot learning. In this work, we propose a novel robust meta-learning algorithm, NestedMAML, which learns to assign weights to training tasks or instances. We consider weights as hyper-parameters and iteratively optimize them using a small set of validation tasks set in a nested bi-level optimization approach (in contrast to the standard bi-level optimization in MAML). We then apply NestedMAML in the meta-training stage, which involves (1) several tasks sampled from a distribution different from the meta-test task distribution, or (2) some data samples with noisy labels. Extensive experiments on synthetic and real-world datasets demonstrate that NestedMAML efficiently mitigates the effects of "unwanted" tasks or instances, leading to significant improvement over the state-of-the-art robust meta-learning methods.
