论文标题
大型少量分类的混乱学习
Confusable Learning for Large-class Few-Shot Classification
论文作者
论文摘要
由于每个班级缺乏足够的样本，因此很少有射击图像分类。当课程数量很大时，即大型少数场景时，这种挑战就变得更加艰难。在这种新颖的情况下，现有的方法表现不佳，因为它们忽略了令人困惑的类别，即很难彼此区分的类似类。这些课程提供更多信息。在本文中，我们提出了一个偏见的学习范式，称为Chopable Learning，该范式更多地集中在可混淆的阶级上。我们的方法可以应用于主流元学习算法。具体而言，我们的方法维护动态更新混淆矩阵，该矩阵分析了数据集中的混乱类。这样的混乱矩阵有助于元学习者强调令人困惑的课程。 Omniglot，Fungi和Imagenet的全面实验证明了我们方法对最先进的基线的功效。
Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., the large-class few-shot scenario. In this novel scenario, existing approaches do not perform well because they ignore confusable classes, namely similar classes that are difficult to distinguish from each other. These classes carry more information. In this paper, we propose a biased learning paradigm called Confusable Learning, which focuses more on confusable classes. Our method can be applied to mainstream meta-learning algorithms. Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset. Such a confusion matrix helps meta learners to emphasize on confusable classes. Comprehensive experiments on Omniglot, Fungi, and ImageNet demonstrate the efficacy of our method over state-of-the-art baselines.
