Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

ICLR 2020 • Chieh-Hsin Lai • Dongmian Zou • Gilad Lerman

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... (read more)

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Tasks
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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Unsupervised Anomaly Detection 20NEWS RSRAE AUC (outlier ratio = 0.5) 0.831 # 1
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Unsupervised Anomaly Detection Caltech-101 RSRAE AUC (outlier ratio = 0.5) 0.772 # 1
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Unsupervised Anomaly Detection Fashion-MNIST RSRAE AUC (outlier ratio = 0.5) 0.833 # 1
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Unsupervised Anomaly Detection Reuters-21578 RSRAE AUC (outlier ratio = 0.5) 0.849 # 1
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Methods used in the Paper
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