论文标题
在黑暗中拍摄：没有基类标签的少数学习
Shot in the Dark: Few-Shot Learning with No Base-Class Labels
论文作者
论文摘要
很少有学习旨在从少数标记的示例中构建新类别的分类器，并且通常通过从一组“基础类”集中访问示例来促进。测试集（新的类）和用于学习归纳偏见的基类之间的数据分布的差异通常会导致对新型类别的概括。为了减轻分配变化引起的问题，先前的研究还探索了新型类别中未标记的示例的使用，除了标记的基本类别的示例，这被称为转导设置。在这项工作中，我们表明，令人惊讶的是，现成的自我监督学习的学习在不使用任何基类标签的情况下，在Miniimagenet上的5-Shot准确性可超过3.9％。这促使我们更仔细地研究了通过自学学习在几次学习中学习的特征的作用。进行了全面的实验，以比较受监督和自我监督特征的可转移性，鲁棒性，效率和互补性。
Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.
