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。

理解和减轻有监督对比学习中的人工标注错误

Understanding and Mitigating Human-Labelling Errors in Supervised  Contrastive Learning

Explainable NLP (ExNLP) has increasingly focused on collecting
human-annotated textual explanations. These explanations are used downstream in
three ways: as data augmentation to improve performance on a predictive task,
as supervision to train models to produce explanations for their predictions,
and as a ground-truth to evaluate model-generated explanations. In this review,
we identify 65 datasets with three predominant classes of textual explanations
(highlights, free-text, and structured), organize the literature on annotating
each type, identify strengths and shortcomings of existing collection
methodologies, and give recommendations for collecting ExNLP datasets in the
future.

本文总结了解释性自然语言处理领域近年来集中收集人类注释的文本解释，将其分为三类 (highlights, free-text, and structured) 并对其注释方法进行整理，提出了现有采集方法的优点和缺点，给出了未来收集数据集的建议。

教我解释：可解释自然语言处理数据集综述

Teach Me to Explain: A Review of Datasets for Explainable Natural  Language Processing

We present a method that learns to answer visual questions by selecting image
regions relevant to the text-based query. Our method exhibits significant
improvements in answering questions such as "what color," where it is necessary
to evaluate a specific location, and "what room," where it selectively
identifies informative image regions. Our model is tested on the VQA dataset
which is the largest human-annotated visual question answering dataset to our
knowledge.

本文提出一种方法，通过选择与基于文本的查询相关的图像区域来学习回答视觉问题。该方法在回答 ' 什么颜色 ' 等需要评估特定位置以及 ' 什么房间 ' 等需要选择性地识别信息图像区域的问题方面展示出显著的提升。我们的模型在 VQA 数据集上进行测试，这是目前为止最大的人工注释视觉问题回答数据集。