Visualization for Multivariate Gaussian Anomaly Detection in Images

• URL: http://arxiv.org/abs/2307.06052v1
• Date: Wed, 12 Jul 2023 10:12:57 GMT
• Title: Visualization for Multivariate Gaussian Anomaly Detection in Images
• Authors: Joao P C Bertoldo and David Arrustico
• Abstract summary: This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly Detection through Instance Modeling) method for anomaly detection in images. We introduce an intermediate step in this framework by applying a whitening transformation to the feature vectors. The results show the importance of visual model validation, providing insights into issues that were otherwise invisible.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly Detection through Instance Modeling) method for anomaly detection in images, fitting a single multivariate Gaussian (MVG) distribution to the feature vectors extracted from a backbone convolutional neural network (CNN) and using their Mahalanobis distance as the anomaly score. We introduce an intermediate step in this framework by applying a whitening transformation to the feature vectors, which enables the generation of heatmaps capable of visually explaining the features learned by the MVG. The proposed technique is evaluated on the MVTec-AD dataset, and the results show the importance of visual model validation, providing insights into issues in this framework that were otherwise invisible. The visualizations generated for this paper are publicly available at https://doi.org/10.5281/zenodo.7937978.

Related papers

• LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space [0.0]
  Masked image modeling is a self-supervised learning technique that predicts the feature representation of masked regions in an image. We propose a novel approach that leverages the characteristics of MIM to detect logical anomalies effectively. We evaluate the proposed method on the MVTecLOCO dataset, achieving an average AUC of 0.867.
  arXiv  Detail & Related papers  (2024-10-14T07:50:56Z)
• Adaptive Graph Convolution Module for Salient Object Detection [7.278033100480174]
  We propose an adaptive graph convolution module (AGCM) to deal with complex scenes. Prototype features are extracted from the input image using a learnable region generation layer. The proposed AGCM dramatically improves the SOD performance both quantitatively and quantitatively.
  arXiv  Detail & Related papers  (2023-03-17T07:07:17Z)
• 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)
• A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection [93.38607559281601]
  We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
  arXiv  Detail & Related papers  (2021-04-29T17:49:48Z)
• Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring [26.434705114982584]
  Grad-CAM is a popular solution that provides such a visualization by combining the activation maps obtained from the model. We introduce a solution to tackle this problem by computing the path integral of the gradient-based terms in Grad-CAM. We conduct a thorough analysis to demonstrate the improvement achieved by our method in measuring the importance of the extracted representations for the CNN's predictions.
  arXiv  Detail & Related papers  (2021-02-15T19:21:46Z)
• Unsupervised Discovery of Disentangled Manifolds in GANs [74.24771216154105]
  Interpretable generation process is beneficial to various image editing applications. We propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adversarial networks.
  arXiv  Detail & Related papers  (2020-11-24T02:18:08Z)
• PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization [64.39761523935613]
  We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding. It also exploits correlations between the different semantic levels of CNN to better localize anomalies.
  arXiv  Detail & Related papers  (2020-11-17T17:29:18Z)
• Anomaly localization by modeling perceptual features [3.04585143845864]
  Feature-Augmented VAE is trained by reconstructing the input image in pixel space, and also in several different feature spaces. It achieves clear improvement over state-of-the-art methods on the MVTec anomaly detection and localization datasets.
  arXiv  Detail & Related papers  (2020-08-12T15:09:13Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Graph-based fusion for change detection in multi-spectral images [16.365393959740718]
  We address the problem of change detection in multi-spectral images by proposing a data-driven framework of graph-based data fusion. We validated our approach in two real cases of remote sensing according to both qualitative and quantitative analyses.
  arXiv  Detail & Related papers  (2020-04-02T02:59:00Z)
• 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)

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.