用谱归一化提升自监督表示学习的可靠性，实现更精准的异常点检测。
Normalizing self-supervised learning for provably reliable Change Point Detection
- 引入谱归一化增强深度表示学习，使嵌入向量更适合变化点检测
- 在三个标准数据集上显著优于当前最先进方法
- 为自监督变化点检测提供理论保障，适合需要高可靠性的场景
变化点检测（CPD）旨在识别输入数据流中分布的突变。准确的估计器在诸多现实场景中至关重要。然而，传统无监督CPD方法受限于强假设或模型表达能力不足。相比之下，表示学习方法通过灵活性和捕捉数据复杂性能力克服这些缺陷。但这类方法在CPD领域仍处于早期阶段，缺乏确保其可靠性的坚实理论基础。本文将表示学习的表达能力与传统CPD方法的稳定性结合，采用谱归一化（SN）进行深度表示学习，并证明经SN处理后的嵌入具有高度信息性。在三个标准CPD数据集上的综合评估显示，该方法显著优于现有最先进方法。
原文摘要 · Abstract (English)
Change point detection (CPD) methods aim to identify abrupt shifts in the distribution of input data streams. Accurate estimators for this task are crucial across various real-world scenarios. Yet, traditional unsupervised CPD techniques face significant limitations, often relying on strong assumptions or suffering from low expressive power due to inherent model simplicity. In contrast, representation learning methods overcome these drawbacks by offering flexibility and the ability to capture the full complexity of the data without imposing restrictive assumptions. However, these approaches are still emerging in the CPD field and lack robust theoretical foundations to ensure their reliability. Our work addresses this gap by integrating the expressive power of representation learning with the groundedness of traditional CPD techniques. We adopt spectral normalization (SN) for deep representation learning in CPD tasks and prove that the embeddings after SN are highly informative for CPD. Our method significantly outperforms current state-of-the-art methods during the comprehensive evaluation via three standard CPD datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。