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.

提出了两种不同的损失函数 ——Proto-Triplet Loss 和 ICNN loss，其中考虑了嵌入向量的重要性，并评估了训练后网络获得的嵌入的质量。在实验中，相比其他度量法，这些损失函数在 miniImagenNet 基准测试中将准确率显著提高了 2％，表明这些损失函数有能力使网络更好地推广到以前未见过的类别。

通过两个新的基于距离的损失函数，强制执行度量学习中的类别可分性，实现少样本图像分类：SuSana Distance 就是你所需要的

SuSana Distancia is all you need: Enforcing class separability in metric  learning via two novel distance-based loss functions for few-shot image  classification