Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches leverage the meta-learning approach, which is encompassed by episodic tasks that make use a support (training) and query set (test) with the objective of learning a similarity comparison metric between those sets. Due to the lack of data, the learning process of the embedding network becomes an important part of the few-shot task. Previous works have addressed this problem using metric learning approaches, but the properties of the underlying latent space and the separability of the difference classes on it was not entirely enforced. In this work, we propose two different loss functions which consider the importance of the embedding vectors by looking at the intra-class and inter-class distance between the few data. The first loss function is the Proto-Triplet Loss, which is based on the original triplet loss with the modifications needed to better work on few-shot scenarios. The second loss function, which we dub ICNN loss is based on an inter and intra class nearest neighbors score, which help us to assess the quality of embeddings obtained from the trained network. Our results, obtained from a extensive experimental setup show a significant improvement in accuracy in the miniImagenNet benchmark compared to other metric-based few-shot learning methods by a margin of 2%, demonstrating the capability of these loss functions to allow the network to generalize better to previously unseen classes. In our experiments, we demonstrate competitive generalization capabilities to other domains, such as the Caltech CUB, Dogs and Cars datasets compared with the state of the art.

翻译：小样本学习是一个具有挑战性的研究领域，旨在仅利用少量标记数据样本学习新概念。近期基于度量学习的方法借鉴了元学习策略，该策略通过包含支持集（训练集）和查询集（测试集）的情节任务来学习两者间的相似性比较度量。由于数据匮乏，嵌入网络的学习过程成为小样本任务的关键环节。先前的研究采用度量学习方法解决此问题，但未能充分强化潜在特征空间的属性及不同类别在该空间中的可分性。本文提出了两种考虑嵌入向量重要性的损失函数，通过分析小样本数据的类内距离与类间距离实现优化。第一种损失函数为原型三元组损失（Proto-Triplet Loss），它在原始三元组损失基础上进行改进，使其更适用于小样本场景。第二种损失函数称为ICNN损失，基于类内与类间最近邻评分，用于评估训练网络所生成嵌入向量的质量。通过广泛的实验设置，我们的结果显示，与其他基于度量的小样本学习方法相比，该方法在miniImageNet基准测试中准确率提升了约2%，证明了这些损失函数能使网络对未见类别具有更强的泛化能力。实验结果表明，该方法在Caltech CUB、Dogs和Cars等数据集上相较于现有技术展现出具有竞争力的跨领域泛化能力。