多级监督对比学习
Multi-level Supervised Contrastive Learning
AI总结：
本文提出多级对比学习（MLCL）框架，通过多个投影头捕捉样本在不同标签和层次上的相似性，适用于多标签和层次分类任务，并在文本和图像数据集上超越现有对比学习方法。
摘要：
对比学习是表示学习中一种成熟的范式。对比学习的标准框架在投影空间中最小化“相似”实例之间的距离，并最大化不相似实例之间的距离，但忽略了两个样本之间可能存在的多种相似性方面。当前方法仅依赖单一投影头，无法捕捉样本不同方面的完整复杂性，导致性能欠佳，尤其是在训练数据有限的场景中。本文在一个统一框架中提出了一种新颖的监督对比学习方法，称为多级对比学习（MLCL），该方法可同时应用于多标签分类和层次分类任务。所提方法的关键优势在于能够利用多个投影头捕捉样本在不同标签和/或层次之间的相似性。在文本和图像数据集上的大量实验表明，所提方法优于当前最先进的对比学习方法。
英文摘要：
Contrastive learning is a well-established paradigm in representation learning. The standard framework of contrastive learning minimizes the distance between "similar" instances and maximizes the distance between dissimilar ones in the projection space, disregarding the various aspects of similarity that can exist between two samples. Current methods rely on a single projection head, which fails to capture the full complexity of different aspects of a sample, leading to suboptimal performance, especially in scenarios with limited training data. In this paper, we present a novel supervised contrastive learning method in a unified framework called multilevel contrastive learning (MLCL), that can be applied to both multi-label and hierarchical classification tasks. The key strength of the proposed method is the ability to capture similarities between samples across different labels and/or hierarchies using multiple projection heads. Extensive experiments on text and image datasets demonstrate that the proposed approach outperforms state-of-the-art contrastive learning methods
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
