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.

翻译：变点检测（CPD）方法旨在识别输入数据流分布中的突变。针对此任务的精确估计器在各种现实场景中至关重要。然而，传统的无监督CPD技术面临显著局限，通常依赖于强假设或因模型固有的简单性而导致表达能力不足。相比之下，表征学习方法通过提供灵活性以及在不施加限制性假设的前提下捕捉数据全部复杂性的能力，克服了这些缺点。然而，这些方法在CPD领域仍处于发展阶段，并且缺乏确保其可靠性的坚实理论基础。我们的工作通过将表征学习的表达能力与传统CPD技术的坚实基础相结合，以弥补这一空白。我们在CPD任务中采用谱归一化（SN）进行深度表征学习，并证明经过SN处理后的嵌入表示对CPD具有高度信息量。通过三个标准CPD数据集的综合评估，我们的方法显著优于当前最先进的方法。