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We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation.
Der massbegriff in der theorie der kontinuierlichen gruppen
Alfred Haar · 1933
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On the generalised distance in statistics
Prasanta Chandra Mahalanobis · 1936
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The approximation of one matrix by another of lower rank
Carl Eckart and Gale Young · 1936
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Extensions of lipschitz mappings into a hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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How to generate random matrices from the classical compact groups
Francesco Mezzadri · 2006
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A note on the weighted harmonic-geometric-arithmetic means inequalities
Gérard Maze and Urs Wagner · 2012
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A review of novelty detection
Marco A F Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Distilling the Knowledge in a Neural Network
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Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Transfer representation-learning for anomaly detection
Jerone Andrews, Thomas Tanay, Edward J Morton, and Lewis D Griffin · 2016
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The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof Choromanski, Mark Rowland, and Adrian Weller · 2017
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Future frame prediction for anomaly detection–a new baseline
Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao · 2018
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Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Niv Cohen and Yedid Hoshen · 2020
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Attention Guided Anomaly Localization in Images
Shashanka Venkataramanan, Kuan Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis · 2020
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Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
Jihun Yi and Sungroh Yoon · 2020
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Deep Learning for Anomaly Detection: A Review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel · 2020
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Multiresolution Knowledge Distillation for Anomaly Detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad Hossein Rohban, and Hamid R. Rabiee · 2021
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Where’s Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
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Improving unsupervised defect segmentation by applying structural similarity to autoencoders
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MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
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