{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:18:32Z","timestamp":1751606312045},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>The detection of anomalous samples in large, high-dimensional datasets is a challenging task with numerous practical applications. Recently, state-of-the-art performance is achieved with deep learning methods: for example, using the reconstruction error from an autoencoder as anomaly scores. However, the scores are uncalibrated: that is, they follow an unknown distribution and lack a clear interpretation. Furthermore, the reconstruction error is highly influenced by the `hardness' of a given sample, which leads to false negative and false positive errors. In this paper, we empirically show the significance of this hardness bias present in a range of recent deep anomaly detection methods. To mitigate this, we propose an efficient and plug-and-play error calibration method which mitigates this hardness bias in the anomaly scoring without the need to retrain the model. We verify the effectiveness of our method on a range of image, time-series, and tabular datasets and against several baseline methods.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/278","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"2002-2008","source":"Crossref","is-referenced-by-count":3,"title":["CADET: Calibrated Anomaly Detection for Mitigating Hardness Bias"],"prefix":"10.24963","author":[{"given":"Ailin","family":"Deng","sequence":"first","affiliation":[{"name":"National University of Singapore"}]},{"given":"Adam","family":"Goodge","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Lang Yi","family":"Ang","sequence":"additional","affiliation":[{"name":"A*STAR, Singapore"}]},{"given":"Bryan","family":"Hooi","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:08:46Z","timestamp":1658128126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/278"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/278","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}