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ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection
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In this paper, we introduce ID-Conditioned Auto-Encoder for unsupervised anomaly detection. Our method is an adaptation of the Class-Conditioned Auto-Encoder (C2AE) designed for the open-set recognition. Assuming that non-anomalous samples constitute of distinct IDs, we apply Conditioned Auto-Encoder with labels provided by these IDs. Opposed to C2AE, our approach omits the classification subtask and reduces the learning process to the single run. We simplify the learning process further by fixing a constant vector as the target for non-matching labels. We apply our method in the context of sounds for machine condition monitoring. We evaluate our method on the ToyADMOS and MIMII datasets from the DCASE 2020 Challenge Task 2. We conduct an ablation study to indicate which steps of our method influences results the most.
Forward citations
Cited by 2 Pith papers
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ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection
ESTM, a dual-branch Mamba with frequency/time patches and a statistical gating module, reports the best average AUC and pAUC on DCASE 2020 Task 2.
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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences
Per-pixel quantile thresholding with excess aggregation matches autoencoder baselines on MIMII sound anomaly detection and provides built-in explanation maps.
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