In machine learning, classifiers are typically susceptible to noise in the training data. In this work, we aim at reducing intra-class noise with the help of graph filtering to improve the classification performance. Considered graphs are obtained by connecting samples of the training set that belong to a same class depending on the similarity of their representation in a latent space. As a matter of fact, by looking at the features in latent representations of samples as graph signals, it is possible to filter them in order to remove high frequencies, thus improving the signal-to-noise ratio. A consequence is that intra-class variance gets smaller, while mean remains the same, as shown theoretically in this article. We support this analysis through experimental evaluation of the graph filtering impact on the accuracy of multiple standard benchmarks of the field. While our approach applies to all classification problems in general, it is particularly useful in few-shot settings, where intra-class noise has a huge impact due to initial sample selection.

翻译：在机器学习中,分类员通常容易在培训数据中出现噪音。 在这项工作中,我们的目标是通过图表过滤来帮助减少阶级内部噪音,以提高分类性能。考虑的图表是通过将属于同一阶级的培训成套样本连接起来获得的,这些样本取决于其在潜在空间的相似性。事实上,通过将样本潜在表现形式的特征作为图示信号来观察,可以过滤它们,以便去除高频率,从而改善信号对噪音的比例。其结果是,阶级内部差异会缩小,而平均值则与本条的理论中显示的相同。我们通过实验性评估图形过滤对多个标准基准的准确性的影响来支持这一分析。我们的方法虽然适用于所有分类问题,但在少数情况下,由于初步抽样选择,类内噪音会产生巨大影响,因此特别有用。