论文标题
从多域表示中选择相关功能以进行几次分类
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
论文作者
论文摘要
几次射击分类的流行方法包括基于大型注释数据集的首先学习通用数据表示，然后将表示形式调整为只有几个标记的样本的新类。在这项工作中，我们提出了一种基于特征选择的新策略，该策略既比以前的特征适应方法更简单，更有效。首先，我们通过训练一组语义上不同的特征提取器来获得多域表示。然后，给定一些学习任务，我们使用多域功能库自动选择最相关的表示形式。我们表明，基于此类功能的简单非参数分类器可产生较高的精度，并概括训练期间从未见过的域，这会导致元数据片的最新结果，并提高了迷你象征的精度。
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.
