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computer-vision-7

Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly

3260 papers • 126 benchmarks • 313 datasets

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STL-10

CIFAR-10

cats_vs_dogs

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Most implemented papers

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman•Fri Mar 29 2019

A neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer) that seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

MNIST

Fashion-MNIST

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Shell Theory: A Statistical Model of Reality

Wen-Yan Lin, Y. Matsushita, Ngai-Man Cheung, Siying Liu, Changhao Ren, Hongdong Li•Thu May 27 2021

The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is rooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process.

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Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection

Wen-Yan Lin, Siying Liu, Zhonghang Liu•Fri Dec 31 2021

A new embedding is proposed using a set of locally varying data projections, with each projection responsible for persever-ing the variations that distinguish a local cluster of instances from all other instances, while simultaneously allowing the probability that an instance belongs to a cluster to be statistically inferred from the one-dimensional, local projection associated with the cluster.

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Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly | State-of-the-Art