通过联合训练提升高速信号异常检测与信号质量，效果显著。
Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals
- 用自编码器与分类器联合训练，学习更清晰的特征表示
- 信号完整性平均提升11.3%，三种算法均优于基线方法
- 适合高速内存信号分析与可靠性优化的研究者使用
本文针对高速动态随机存取存储器信号中的异常检测与信号完整性问题，提出一种联合训练框架，将自编码器与分类器结合，聚焦有效数据特征以学习更具区分性的潜在表示。该方法在三种异常检测算法上均优于两种基线方法，且消融实验进一步验证其有效性。此外，提出的信号完整性增强算法使信号质量平均提升11.3%。相关源代码与数据已公开于 https://github.com/Usama1002/learning-latent-representations。
原文摘要 · Abstract (English)
This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations.
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