Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combined with available metadata to solve multiple pretext tasks that learn a good representation of data. We apply our approach on respiratory sound classification. This task is suited for this setting as demographic information such as sex and age are correlated with presence of lung diseases, and learning a system that implicitly encode this information may better detect anomalies. Supervised contrastive learning is a paradigm that learns similar representations to samples sharing the same class labels and dissimilar representations to samples with different class labels. The feature extractor learned using this paradigm extract useful features from the data, and we show that it outperforms cross-entropy in classifying respiratory anomalies in two different datasets. We also show that learning representations using only metadata, without class labels, obtains similar performance as using cross entropy with those labels only. In addition, when combining class labels with metadata using multiple supervised contrastive learning, an extension of supervised contrastive learning solving an additional task of grouping patients within the same sex and age group, more informative features are learned. This work suggests the potential of using multiple metadata sources in supervised contrastive settings, in particular in settings with class imbalance and few data. Our code is released at https://github.com/ilyassmoummad/scl_icbhi2017

翻译：摘要：基于端到端方式使用标注的有监督学习方法一直是分类问题的最先进技术。然而，这些方法在泛化能力上可能受限，尤其是在低数据场景下。在本研究中，我们通过结合可用元数据的监督对比学习来解决这一问题，以完成多个前置任务，从而学习数据的优质表示。我们将该方法应用于呼吸音分类。此任务适合该设置，因为性别和年龄等人口统计信息与肺部疾病的存在相关，且学习一个隐式编码这些信息的系统能更好地检测异常。监督对比学习是一种范式，它使共享相同类标签的样本学习到相似表示，而不同类标签的样本学习到不同表示。使用该范式学到的特征提取器能从数据中提取有用特征，我们证明其在两个不同数据集的呼吸异常分类中优于交叉熵。我们还表明，仅使用元数据而无需类标签学习表示，其性能与仅使用这些标签的交叉熵相当。此外，当通过多重监督对比学习（一种扩展的监督对比学习，额外解决将患者按相同性别和年龄分组任务）结合类标签和元数据时，能学到更具信息量的特征。这项工作揭示了在监督对比设置中利用多种元数据源的潜力，尤其是在类别不平衡和少量数据的情况下。我们的代码已发布于https://github.com/ilyassmoummad/scl_icbhi2017