弱监督学习的最新进展：新的监督范式、假设松弛与实用解决方案
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
AI总结：
该文针对弱监督学习领域的实际需求，提出置信度差异分类的解决方法、更宽松假设下的互补标签学习方案，并给出部分标签学习的公平评估框架，推动了该领域的发展。
AI中文摘要：
近年来，得益于高质量、标注完善的训练数据的可用性，深度学习取得了巨大成功。但在实际应用中，这一要求往往无法满足。弱监督学习旨在利用不完整、不精确或不准确的监督来训练准确的模型。在本章中，我们将讨论该领域的最新进展，包括新的监督范式、松弛的假设以及实用解决方案。首先，我们介绍一种名为置信度差异分类的新型弱监督二分类问题，并提出一致的方法来解决它。接下来，我们研究互补标签学习，这是一种弱监督多分类问题，我们提出的方法基于对数据生成过程比现有一致方法更宽松的假设。最后，我们提出了针对部分标签学习（另一种流行的多分类弱监督学习问题）的评估框架，以促进该领域算法的公平和现实评估。
英文摘要：
Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this requirement is often not met in real-world applications. Weakly supervised learning aims to train an accurate model with incomplete, inexact, or inaccurate supervision. In this chapter, we will discuss recent advances in this field, including new supervision paradigms, relaxed assumptions, and practical solutions. First, we introduce a new weakly supervised binary classification problem called confidence-difference classification and propose consistent approaches to solve it. Next, we investigate complementary-label learning, a weakly supervised multi-class classification problem. Our proposed approaches are based on more relaxed assumptions about the data generation process than existing consistent approaches. Lastly, we present an evaluation framework for partial-label learning, another popular multi-class weakly supervised learning problem, in order to promote fair and realistic evaluation of algorithms in this field.
