Projects per year
Detecting semantic anomalies is challenging due to the countless ways in which they may appear in real-world data. While enhancing the robustness of networks may be sufficient for modeling simplistic anomalies, there is no good known way of preparing models for all potential and unseen anomalies that can potentially occur, such as the appearance of new object classes. In this paper, we show that a previously overlooked strategy for anomaly detection (AD) is to introduce an explicit inductive bias toward representations transferred over from some large and varied semantic task. We rigorously verify our hypothesis in controlled trials that utilize intervention, and show that it gives rise to surprisingly effective auxiliary objectives that outperform previous AD paradigms.
|Title of host publication||Proceedings of the 38th International Conference on Machine Learning|
|Publication status||Published - 18 Jul 2021|
|Event||Thirty-eighth International Conference on Machine Learning - Online|
Duration: 18 Jul 2021 → 24 Jul 2021
|Name||Proceedings of Machine Learning Research|
|Conference||Thirty-eighth International Conference on Machine Learning|
|Abbreviated title||ICML 2021|
|Period||18/07/21 → 24/07/21|
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- 1 Active
1/12/20 → 31/05/23