论文标题
嘈杂的标签很少学习
Few-shot Learning with Noisy Labels
论文作者
论文摘要
在新课程上训练时，很少有射击学习（FSL）方法通常假设具有准确标记的样品的清洁支持集。这个假设通常可能是不现实的：支持集，无论多么小，仍然都会包含标签错误的样本。因此，对标签噪声的鲁棒性对于FSL方法是实用的，但是这个问题令人惊讶地在很大程度上没有探索。为了解决FSL设置中标签错误的样本，我们做出了一些技术贡献。 （1）我们提供了简单但有效的特征聚合方法，改善了流行的FSL技术Protonet使用的原型。 （2）我们描述了一种嘈杂的噪声学习（TRANF）的新型变压器模型。 Tranfs利用变压器的注意机制称重标签错误与正确样本。 （3）最后，我们在嘈杂的迷你胶原和tieredimagenet上广泛测试了这些方法。我们的结果表明，TRANFS与清洁支持集的领先FSL方法相对应，但到目前为止，在存在标签噪声的情况下，它们的表现优于它们。
Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be practical, but this problem surprisingly remains largely unexplored. To address mislabeled samples in FSL settings, we make several technical contributions. (1) We offer simple, yet effective, feature aggregation methods, improving the prototypes used by ProtoNet, a popular FSL technique. (2) We describe a novel Transformer model for Noisy Few-Shot Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to weigh mislabeled versus correct samples. (3) Finally, we extensively test these methods on noisy versions of MiniImageNet and TieredImageNet. Our results show that TraNFS is on-par with leading FSL methods on clean support sets, yet outperforms them, by far, in the presence of label noise.
