arXiv cs.AI· Antonia Holzapfel, Andres Felipe Posada Moreno, Sebastian Trimpe·· 1 天前AI 评分24
CENDRe：用自然域表示做概念提取的 CNN 时序分类方法
CENDRe: Concept Extraction with Natural Domain Representations
AI 导读
CENDRe 是一种面向 CNN 时序分类的概念提取方法，通过两阶段聚类每时间步潜在表示并用轮廓系数引导自动确定概念数量，解决现有方法仅限时域、依赖预设概念数、定位不准三个问题。它用存在分数及其梯度生成掩码，并借助傅里叶变换等可逆映射把同一概念定位到不同信号域。在合成基准上表示正确性媲美 SOTA 方法且重要性正确性更高，在真实轴承故障数据上提取出驱动预测的频段。
正文 · 原文
Abstract:Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical contexts requires understanding the patterns across signal domains that drive their predictions (e.g., temporal or spectral). Concept extraction (CE) methods identify such patterns by analyzing representations within the models' latent space. However, existing time-series CE methods have three limitations: they operate only in the time domain and cannot locate features in other domains, they depend on a predefined number of concepts that biases them, and they produce localizations misaligned with the regions the model uses. We address these limitations with CENDRe, a concept extraction method for CNNs. For concept discovery, we propose a procedure that clusters per-timestep latent representations in two stages, with a silhouette-guided aggregation that selects the number of concepts automatically. For localization, we define a presence score that contrasts the latent representations with their prototypes, and use its gradients to produce masks that concentrate on the regions driving the concept. These gradients, propagated through a differentiable invertible mapping of the input such as a Fourier transform, yield localizations for the same concepts in different domains. Finally, each concept receives a relevance score that quantifies its contribution to each class. On synthetic benchmarks, CENDRe achieves representation correctness comparable to state-of-the-art CE methods and higher importance correctness, while producing visualizations in different domains. On real bearing-fault data, CENDRe extracts the frequency bands driving the model's predictions, located in regions commonly inspected for fault diagnosis, producing evidence to assess the model that time-domain CE methods cannot.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.29621 [cs.LG] |
| (or arXiv:2607.29621v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.29621 arXiv-issued DOI via DataCite |
Submission history
From: Antonia Holzapfel [view email]
[v1]
Fri, 31 Jul 2026 16:56:27 UTC (17,026 KB)
[v2]
Wed, 11 Nov 2026 17:20:54 UTC (17,026 KB)
来源：arXiv cs.AI · arxiv.org