Wang Rui-Qi, Zhang Xu-Yao, Liu Cheng-Lin
IEEE Trans Neural Netw Learn Syst. 2022 Nov;33(11):6990-6996. doi: 10.1109/TNNLS.2021.3083650. Epub 2022 Oct 27.
Few-shot learning (FSL) aims to classify novel images based on a few labeled samples with the help of meta-knowledge. Most previous works address this problem based on the hypothesis that the training set and testing set are from the same domain, which is not realistic for some real-world applications. Thus, we extend FSL to domain-agnostic few-shot recognition, where the domain of the testing task is unknown. In domain-agnostic few-shot recognition, the model is optimized on data from one domain and evaluated on tasks from different domains. Previous methods for FSL mostly focus on learning general features or adapting to few-shot tasks effectively. They suffer from inappropriate features or complex adaptation in domain-agnostic few-shot recognition. In this brief, we propose meta-prototypical learning to address this problem. In particular, a meta-encoder is optimized to learn the general features. Different from the traditional prototypical learning, the meta encoder can effectively adapt to few-shot tasks from different domains by the traces of the few labeled examples. Experiments on many datasets demonstrate that meta-prototypical learning performs competitively on traditional few-shot tasks, and on few-shot tasks from different domains, meta-prototypical learning outperforms related methods.
少样本学习（FSL）旨在借助元知识，基于少量有标签样本对新图像进行分类。先前的大多数工作基于训练集和测试集来自同一领域这一假设来解决此问题，而这对于某些实际应用来说并不现实。因此，我们将FSL扩展到领域无关的少样本识别，即测试任务的领域是未知的。在领域无关的少样本识别中，模型在来自一个领域的数据上进行优化，并在来自不同领域的任务上进行评估。先前的FSL方法大多专注于学习通用特征或有效适应少样本任务。在领域无关的少样本识别中，它们存在特征不合适或适应过程复杂的问题。在本简报中，我们提出元原型学习来解决此问题。具体而言，优化一个元编码器以学习通用特征。与传统的原型学习不同，元编码器可以通过少量有标签示例的痕迹有效适应来自不同领域的少样本任务。在许多数据集上的实验表明，元原型学习在传统少样本任务上具有竞争力，并且在来自不同领域的少样本任务上，元原型学习优于相关方法。
