CAPMix combines CutAddPaste anomaly injection, DTW-based label revision, and dual-space mixup to improve time-series anomaly detection, reporting gains over prior methods on five benchmarks.
Rethinking Assumptions in Deep Anomaly Detection
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abstract
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous." In this paper we present results demonstrating that this intuition surprisingly seems not to extend to deep AD on images. For a recent AD benchmark on ImageNet, classifiers trained to discern between normal samples and just a few (64) random natural images are able to outperform the current state of the art in deep AD. Experimentally we discover that the multiscale structure of image data makes example anomalies exceptionally informative.
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CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments
CAPMix combines CutAddPaste anomaly injection, DTW-based label revision, and dual-space mixup to improve time-series anomaly detection, reporting gains over prior methods on five benchmarks.