基于LSSVM基本学习器和转换模块的少样本学习
Few-shot Learning with LSSVM Base Learner and Transductive Modules
摘要：
用于少样本学习的元学习方法的性能通常取决于三个方面：适合比较的特征、适合低数据场景的分类器（基础学习器）以及来自待分类样本的有价值信息。本文针对后两个方面进行了改进：1）虽然已有很多有效的基学习器，但在泛化性能和计算开销之间存在一个折衷，因此我们引入了多类最小二乘支持向量机作为基学习器，它以较小的计算开销获得了比现有基学习器更好的泛化性能; 2）为了更好地利用查询样本中的信息，提出了两个简单有效的转换模块，利用查询样本修改支持集，即：基于所述注意力机制调整所述支持样本，并将带有伪标签的查询集的原型作为伪支持样本添加到所述支持集中。这两个模块显著提高了Few-Shot分类精度，特别是对于困难的1-shot设置。我们的模型，表示为FSLSTM（少样本学习与LSsvm基础学习器和转换模块），在miniImageNet和CIFAR-FS少样本学习基准测试中达到了最先进的性能。
英文摘要：
The performance of meta-learning approaches for few-shot learning generally depends on three aspects: features suitable for comparison, the classifier ( base learner ) suitable for low-data scenarios, and valuable information from the samples to classify. In this work, we make improvements for the last two aspects: 1) although there are many effective base learners, there is a trade-off between generalization performance and computational overhead, so we introduce multi-class least squares support vector machine as our base learner which obtains better generation than existing ones with less computational overhead; 2) further, in order to utilize the information from the query samples, we propose two simple and effective transductive modules which modify the support set using the query samples, i.e., adjusting the support samples basing on the attention mechanism and adding the prototypes of the query set with pseudo labels to the support set as the pseudo support samples. These two modules significantly improve the few-shot classification accuracy, especially for the difficult 1-shot setting. Our model, denoted as FSLSTM (Few-Shot learning with LSsvm base learner and Transductive Modules), achieves state-of-the-art performance on miniImageNet and CIFAR-FS few-shot learning benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
