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. We show that the proposed graph filtering methodology has the effect of asymptotically reducing intra-class variance, while maintaining the mean. While our approach applies to all classification problems in general, it is particularly useful in few-shot settings, where intra-class noise can have a huge impact due to the small sample selection. Using standardized benchmarks in the field of vision, we empirically demonstrate the ability of the proposed method to slightly improve state-of-the-art results in both cases of few-shot and standard classification.
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,这些样本取决于他们在潜在空间中的代表性的相似性。我们表明,拟议的图表过滤方法具有不同时减少阶级内部差异的作用,同时保持这一平均值。虽然我们的方法一般适用于所有分类问题,但在几发环境中却特别有用,因为在少数样本选择的情况下,阶级内部噪音会产生巨大影响。我们在视觉领域使用标准化基准,从经验上展示了拟议方法在微小的和标准分类中略微改进最新结果的能力。