这篇论文提出了CENDRe，给时间序列CNN做概念提取，能自动找概念数量，还能同时定位时域和频域，比只看时域的方法更靠谱。
CENDRe是一种面向CNN时间序列分类器的概念提取方法，自动发现驱动预测的时域和频域模式。它通过两阶段聚类和轮廓系数指导的聚合自动确定概念数量，无需预设。方法利用存在分数的梯度生成时域掩码，并经可逆傅里叶变换映射到频域，实现同一概念的频域定位。在合成基准上，CENDRe的表示正确性与现有方法相当，重要性正确性显著更高。在真实轴承故障数据上，它提取出驱动模型预测的频带，为故障诊断提供时域方法无法给出的证据。
CENDRe: Concept Extraction with Natural Domain Representations
Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical domains requires understanding the temporal and spectral patterns that drive their predictions. 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 overlook frequency features, predefine the number of concepts, and produce localizations misaligned with the regions the model uses. We address these limitations by proposing CENDRe, a concept extraction method for CNNs. It first discovers concepts by clustering per-timestep latent representations in two stages, where silhouette-guided aggregation selects the number of concepts automatically. Then, it localizes each concept through gradients of a presence score that contrasts the latent representations with their prototypes, producing 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 the frequency domain. 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 significantly higher importance correctness. 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.