The estimation of the generalization error of classifiers often relies on a
validation set. Such a set is hardly available in few-shot learning scenarios,
a highly disregarded shortcoming in the field. In these scenarios, it is common
to rely on features extracted from pre-trained neural networks combined with
distance-based classifiers such as nearest class mean. In this work, we
introduce a Gaussian model of the feature distribution. By estimating the
parameters of this model, we are able to predict the generalization error on
new classification tasks with few samples. We observe that accurate distance
estimates between class-conditional densities are the key to accurate estimates
of the generalization performance. Therefore, we propose an unbiased estimator
for these distances and integrate it in our numerical analysis. We empirically
show that our approach outperforms alternatives such as the leave-one-out
cross-validation strategy.

本文提出了一种基于高斯模型的特征分布参数估计方法，通过估计类间条件密度之间的距离实现对于少样本分类任务中泛化误差的预测，并在实验中表明该方法优于 leave-one-out cross-validation 等传统方法。

少样本分类通用性预测的统计模型

A Statistical Model for Predicting Generalization in Few-Shot Classification

In open-set recognition (OSR), classifiers should be able to reject
unknown-class samples while maintaining high closed-set classification
accuracy. To effectively solve the OSR problem, previous studies attempted to
limit latent feature space and reject data located outside the limited space
via offline analyses, e.g., distance-based feature analyses, or complicated
network architectures. To conduct OSR via a simple inference process (without
offline analyses) in standard classifier architectures, we use distance-based
classifiers instead of conventional Softmax classifiers. Afterwards, we design
a background-class regularization strategy, which uses background-class data as
surrogates of unknown-class ones during training phase. Specifically, we
formulate a novel regularization loss suitable for distance-based classifiers,
which reserves sufficiently large class-wise latent feature spaces for known
classes and forces background-class samples to be located far away from the
limited spaces. Through our extensive experiments, we show that the proposed
method provides robust OSR results, while maintaining high closed-set
classification accuracy.

本研究提出在标准分类器结构中使用基于距离的分类器，通过背景类正则化策略来增强开放集识别的鲁棒性，从而避免了复杂的离线分析和网络设计，同时保持高准确性的闭集分类。