Quantum Normalizing Flows for Anomaly Detection

• URL: http://arxiv.org/abs/2402.02866v3
• Date: Mon, 22 Jul 2024 14:18:42 GMT
• Title: Quantum Normalizing Flows for Anomaly Detection
• Authors: Bodo Rosenhahn, Christoph Hirche,
• Abstract summary: We introduce Normalizing Flows for Quantum architectures, describe how to model and optimize such a flow and evaluate our method on example datasets. Our proposed models show competitive performance for anomaly detection compared to classical methods. In the experiments we compare our performance to isolation forests (IF), the local outlier factor (LOF) or single-class SVMs.
• Score: 23.262276593120305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g. anomaly detection, once such a mapping has been learned. In this work we introduce Normalizing Flows for Quantum architectures, describe how to model and optimize such a flow and evaluate our method on example datasets. Our proposed models show competitive performance for anomaly detection compared to classical methods, esp. those ones where there are already quantum inspired algorithms available. In the experiments we compare our performance to isolation forests (IF), the local outlier factor (LOF) or single-class SVMs.

Related papers

• Stochastic Sampling from Deterministic Flow Models [8.849981177332594]
  We present a method to turn flow models into a family of differential equations (SDEs) that have the same marginal distributions. We empirically demonstrate advantages of our method on a toy Gaussian setup and on the large scale ImageNet generation task.
  arXiv  Detail & Related papers  (2024-10-03T05:18:28Z)
• VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization [101.41553763861381]
  This paper explores the potential of normalizing flows in multi-class anomaly detection. We empower the flow models to distinguish different concepts of multi-class normal data in an unsupervised manner, resulting in a novel flow-based unified method, named VQ-Flow. The proposed VQ-Flow advances the state-of-the-art in multi-class anomaly detection within a unified training scheme, yielding the Det./Loc. AUROC of 99.5%/98.3% on MVTec AD.
  arXiv  Detail & Related papers  (2024-09-02T05:01:41Z)
• Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories [6.222204646855336]
  We study the consequences of mode-collapse of normalizing flows in the context of lattice field theory. We propose a metric to quantify the degree of mode-collapse and derive a bound on the resulting bias.
  arXiv  Detail & Related papers  (2023-02-27T19:00:22Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• VQ-Flows: Vector Quantized Local Normalizing Flows [2.7998963147546148]
  We introduce a novel statistical framework for learning a mixture of local normalizing flows as "chart maps" over a data manifold. Our framework augments the expressivity of recent approaches while preserving the signature property of normalizing flows, that they admit exact density evaluation.
  arXiv  Detail & Related papers  (2022-03-22T09:22:18Z)
• Efficient CDF Approximations for Normalizing Flows [64.60846767084877]
  We build upon the diffeomorphic properties of normalizing flows to estimate the cumulative distribution function (CDF) over a closed region. Our experiments on popular flow architectures and UCI datasets show a marked improvement in sample efficiency as compared to traditional estimators.
  arXiv  Detail & Related papers  (2022-02-23T06:11:49Z)
• Resampling Base Distributions of Normalizing Flows [0.0]
  We introduce a base distribution for normalizing flows based on learned rejection sampling. We develop suitable learning algorithms using both maximizing the log-likelihood and the optimization of the reverse Kullback-Leibler divergence.
  arXiv  Detail & Related papers  (2021-10-29T14:44:44Z)
• Gaussianization Flows [113.79542218282282]
  We propose a new type of normalizing flow model that enables both efficient iteration of likelihoods and efficient inversion for sample generation. Because of this guaranteed expressivity, they can capture multimodal target distributions without compromising the efficiency of sample generation.
  arXiv  Detail & Related papers  (2020-03-04T08:15:06Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)
• Learning Likelihoods with Conditional Normalizing Flows [54.60456010771409]
  Conditional normalizing flows (CNFs) are efficient in sampling and inference. We present a study of CNFs where the base density to output space mapping is conditioned on an input x, to model conditional densities p(y|x)
  arXiv  Detail & Related papers  (2019-11-29T19:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.