多层级投影头提升对比学习，尤其在小数据下表现更优
Multi-level Supervised Contrastive Learning
- 用多个投影头捕捉样本间多维度相似性
- 在文本与图像数据集上超越现有对比学习方法
- 适合标签复杂或训练数据少的任务场景
对比学习是表征学习中的主流范式，标准框架通过拉近相似样本、推远不相似样本的距离来学习特征。然而，现有方法通常仅依赖单一投影头，无法充分捕捉样本间多种层次的相似性，尤其在训练数据有限时性能受限。本文提出一种统一框架下的多层级对比学习（MLCL），可应用于多标签和层次分类任务。其核心思想是利用多个投影头，从不同标签或层级视角建模样本间的相似关系。在文本与图像数据集上的大量实验表明，该方法显著优于当前最优对比学习模型。
原文摘要 · Abstract (English)
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
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