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arXiv Machine Learning By Aditi Gautam, Manikandan Ravikiran, Saira Qureshi, Sena Fatma Ekiz, Charan Jagwani, Mahmoud Elhadidy, Sanjeev Roka

NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection

Read the original on arXiv Machine Learning →

NV-Tesseract-AD is a curriculum learning‑enhanced conditional diffusion framework designed for multivariate time‑series anomaly detection. It structures reconstruction difficulty along masking severity and diffusion noise level, using a Transformer‑based diffusion backbone and a three‑stage dual curriculum that keeps anomalous samples throughout training. The method achieves significant performance gains, including a 3.7‑fold F1 improvement and 56% MSE reduction over a non‑curriculum baseline, and demonstrates strong zero‑shot results on several benchmark families.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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