Abstract
We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a “manifold” close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.
Original language | English (US) |
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State | Published - 2020 |
Event | 8th International Conference on Learning Representations, ICLR 2020 - Addis Ababa, Ethiopia Duration: Apr 30 2020 → … |
Conference
Conference | 8th International Conference on Learning Representations, ICLR 2020 |
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Country/Territory | Ethiopia |
City | Addis Ababa |
Period | 4/30/20 → … |
Bibliographical note
Publisher Copyright:© 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.