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LEAP: a Latent Embedding Anomaly Patch-Transformer for time-series anomaly detection
Time-series anomaly detection is dominated by reconstruction-based neural detectors, whose objective is structurally flawed. Since the target is also the input, an expressive model can learn to reconstruct anomalies too, leaving uninformative residuals. Recent large-scale benchmarks show that, on cu
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