# ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection

arXiv: https://arxiv.org/abs/2007.05314 · HTML: https://mlsanity.com/paper/2007.05314/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions](https://mlsanity.com/paper/2106.04492/) (2021)
- [MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions](https://mlsanity.com/paper/2105.02702/) (2021)
- [Anomalous Sound Detection with Machine Learning: A Systematic Review](https://mlsanity.com/paper/2102.07820/) (2021)
- [Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?](https://mlsanity.com/paper/2309.15643/) (2023)
- [Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks](https://mlsanity.com/paper/2409.05319/) (2024)
- [ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection](https://mlsanity.com/paper/2509.02471/) (2025)
- [TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detection](https://mlsanity.com/paper/2602.01060/) (2026)

## Abstract (arXiv)

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.