Human-annotated vision datasets inevitably contain a fraction of human
mislabelled examples. While the detrimental effects of such mislabelling on
supervised learning are well-researched, their influence on Supervised
Contrastive Learning (SCL) remains largely unexplored. In this paper, we show
that human-labelling errors not only differ significantly from synthetic label
errors, but also pose unique challenges in SCL, different to those in
traditional supervised learning methods. Specifically, our results indicate
they adversely impact the learning process in the ~99% of cases when they occur
as false positive samples. Existing noise-mitigating methods primarily focus on
synthetic label errors and tackle the unrealistic setting of very high
synthetic noise rates (40-80%), but they often underperform on common image
datasets due to overfitting. To address this issue, we introduce a novel SCL
objective with robustness to human-labelling errors, SCL-RHE. SCL-RHE is
designed to mitigate the effects of real-world mislabelled examples, typically
characterized by much lower noise rates (<5%). We demonstrate that SCL-RHE
consistently outperforms state-of-the-art representation learning and
noise-mitigating methods across various vision benchmarks, by offering improved
resilience against human-labelling errors.

人工标注的视觉数据集中不可避免地包含一部分人为错误标签样本。我们的研究表明，人工标注错误不仅与合成标签错误有明显不同，而且对于监督对比学习 (SCL) 而言也具有独特的挑战。为了应对这个问题，我们引入了一种对人工标注错误具有鲁棒性的新型 SCL 目标函数，SCL-RHE。通过提供对人工标注错误的改进稳健性，我们证明 SCL-RHE 在各种视觉基准测试中始终优于最先进的表示学习和噪声缓解方法。