Transfer-Based Semantic Anomaly Detection

Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:2546-2558, 2021.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-deecke21a, title = {Transfer-Based Semantic Anomaly Detection}, author = {Deecke, Lucas and Ruff, Lukas and Vandermeulen, Robert A. and Bilen, Hakan}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {2546--2558}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/deecke21a/deecke21a.pdf}, url = {https://proceedings.mlr.press/v139/deecke21a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Transfer-Based Semantic Anomaly Detection %A Lucas Deecke %A Lukas Ruff %A Robert A. Vandermeulen %A Hakan Bilen %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-deecke21a %I PMLR %P 2546--2558 %U https://proceedings.mlr.press/v139/deecke21a.html %V 139 %X 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.
APA
Deecke, L., Ruff, L., Vandermeulen, R.A. & Bilen, H.. (2021). Transfer-Based Semantic Anomaly Detection. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:2546-2558 Available from https://proceedings.mlr.press/v139/deecke21a.html.

Related Material