标题： 标签错误如何影响对比学习？ 来自数据降维的视角 显示英文标题

标题： How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction

摘要： 近年来，对比学习在自监督表示学习领域取得了最先进的性能。 许多先前的工作尝试提供对对比学习成功背后的理论理解。 几乎所有的工作都依赖于一个默认假设，即标签一致性假设，由于常见的增强策略（如随机缩放裁剪，RRC）的强度和随机性，该假设在实践中可能不成立（失败的概率称为标签错误）。 本文研究了标签错误对对比学习下游分类性能的理论影响。 我们首先揭示了标签错误对下游分类风险的几个显著负面影响。 为了减轻这些影响，在原始数据上应用数据维度约简方法（例如，奇异值分解，SVD）以减少误报样本，并建立理论和实证评估。 此外，还发现SVD是一把双刃剑，可能由于增强图的连通性降低而导致下游分类准确性的下降。 基于上述观察，我们给出了增强建议，即我们应该使用一些适度的嵌入维度（如我们实验中的$512, 1024$）、数据扩充、弱增强和SVD，以确保大图连通性和小标签错误，从而提高模型性能。 显示英文摘要

摘要： In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning. Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely on a default assumption, i.e., the label consistency assumption, which may not hold in practice (the probability of failure is called labeling error) due to the strength and randomness of common augmentation strategies, such as random resized crop (RRC). This paper investigates the theoretical impact of labeling error on the downstream classification performance of contrastive learning. We first reveal several significant negative impacts of labeling error on downstream classification risk. To mitigate these impacts, data dimensionality reduction method (e.g., singular value decomposition, SVD) is applied on original data to reduce false positive samples, and establish both theoretical and empirical evaluations. Moreover, it is also found that SVD acts as a double-edged sword, which may lead to the deterioration of downstream classification accuracy due to the reduced connectivity of the augmentation graph. Based on the above observations, we give the augmentation suggestion that we should use some moderate embedding dimension (such as $512, 1024$ in our experiments), data inflation, weak augmentation, and SVD to ensure large graph connectivity and small labeling error to improve model performance.