Prototype is widely used to represent internal structure of category for few-shot learning, which was proposed as a simple inductive bias to address the issue of overfitting. However, since prototype representation is normally averaged from individual samples, it can appropriately to represent some classes but with underfitting to represent some others that can be batter represented by exemplars. To address this problem, in this work, we propose Shrinkage Exemplar Networks (SENet) for few-shot classification. In SENet, categories are represented by the embedding of samples that shrink towards their mean via spectral filtering. Furthermore, a shrinkage exemplar loss is proposed to replace the widely used cross entropy loss for capturing the information of individual shrinkage samples. Several experiments were conducted on miniImageNet, tiered-ImageNet and CIFAR-FS datasets. The experimental results demonstrate the effectiveness of our proposed method.

翻译：原型被广泛用于表示小样本学习中类别的内部结构，其作为简单的归纳偏置提出以解决过拟合问题。然而，由于原型表示通常从单个样本平均得到，它能够恰当表示某些类别，但对另一些更适合通过示例表示的类别存在欠拟合问题。针对这一问题，本文提出收缩示例网络（SENet）用于小样本分类。在SENet中，类别通过样本嵌入表示，这些嵌入通过频谱滤波向其均值收缩。此外，提出收缩示例损失函数替代广泛使用的交叉熵损失，以捕获单个收缩样本的信息。在miniImageNet、tiered-ImageNet和CIFAR-FS数据集上进行了多项实验，实验结果验证了所提方法的有效性。