miCSE：用于低样本句子嵌入的互信息对比学习
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings
AI总结：
本文提出基于互信息的对比学习框架miCSE，通过强制不同视图间注意力模式对齐与结构一致性，提升了对比自监督学习的样本效率，在少样本句子嵌入任务上取得优异表现。
摘要：
本文提出了miCSE，一个基于互信息的对比学习框架，显著推进了少样本句子嵌入的技术水平。该方法在对比学习过程中，强制不同视图间的注意力模式对齐。使用miCSE学习句子嵌入需要为每个句子强制跨增强视图的结构一致性，使对比自监督学习更高效。结果表明，该方法在少样本学习领域表现优异。在少样本学习的多个基准上，其结果优于现有方法，在全样本场景下也具可比性。本研究为比现有对比方法更鲁棒的自监督学习开辟了新途径。
英文摘要：
This paper presents miCSE, a mutual information-based contrastive learning framework that significantly advances the state-of-the-art in few-shot sentence embedding. The proposed approach imposes alignment between the attention pattern of different views during contrastive learning. Learning sentence embeddings with miCSE entails enforcing the structural consistency across augmented views for every sentence, making contrastive self-supervised learning more sample efficient. As a result, the proposed approach shows strong performance in the few-shot learning domain. While it achieves superior results compared to state-of-the-art methods on multiple benchmarks in few-shot learning, it is comparable in the full-shot scenario. This study opens up avenues for efficient self-supervised learning methods that are more robust than current contrastive methods for sentence embedding.
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。
