Unsupervised Anomaly Detection and Localisation with Multi-scale Interpolated Gaussian Descriptors

• URL: http://arxiv.org/abs/2101.10043v1
• Date: Mon, 25 Jan 2021 12:38:51 GMT
• Title: Unsupervised Anomaly Detection and Localisation with Multi-scale Interpolated Gaussian Descriptors
• Authors: Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro
• Abstract summary: We introduce an unsupervised anomaly detection and localisation method designed to address two issues. We introduce a normal image distribution estimation method that is robust to under-represented classes of normal images. We also propose a new anomaly identification criterion that can accurately detect and localise multi-scale structural and non-structural anomalies.
• Score: 31.02818044068126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current unsupervised anomaly detection and localisation systems are commonly formulated as one-class classifiers that depend on an effective estimation of the distribution of normal images and robust criteria to identify anomalies. However, the distribution of normal images estimated by current systems tends to be unstable for classes of normal images that are under-represented in the training set, and the anomaly identification criteria commonly explored in the field does not work well for multi-scale structural and non-structural anomalies. In this paper, we introduce an unsupervised anomaly detection and localisation method designed to address these two issues. More specifically, we introduce a normal image distribution estimation method that is robust to under-represented classes of normal images -- this method is based on adversarially interpolated descriptors from training images and a Gaussian classifier. We also propose a new anomaly identification criterion that can accurately detect and localise multi-scale structural and non-structural anomalies. In extensive experiments on MNIST, Fashion MNIST, CIFAR10 and MVTec AD data sets, our approach shows better results than the current state of the arts in the standard experimental setup for unsupervised anomaly detection and localisation. Code is available at https://github.com/tianyu0207/IGD.

Related papers

• GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [60.78684630040313]
  Diffusion models tend to reconstruct normal counterparts of test images with certain noises added. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. We propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2024-06-11T17:27:23Z)
• Anomaly Detection by Context Contrasting [57.695202846009714]
  Anomaly detection focuses on identifying samples that deviate from the norm. Recent advances in self-supervised learning have shown great promise in this regard. We propose Con$$, which learns through context augmentations.
  arXiv  Detail & Related papers  (2024-05-29T07:59:06Z)
• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image [63.59114880750643]
  We introduce a novel Spatial-aware Attention Generative Adrialversa Network (SAGAN) for one-class semi-supervised generation of health images. SAGAN generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images. Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-21T15:41:34Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization [2.8659934481869715]
  Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring anomalous data during training. Most current state-of-the-art methods use latent variable generative models operating directly on the images. We propose CRADL whose core idea is to model the distribution of normal samples directly in the low-dimensional representation space of an encoder trained with a contrastive pretext-task.
  arXiv  Detail & Related papers  (2023-01-05T16:07:49Z)
• Prototypical Residual Networks for Anomaly Detection and Localization [80.5730594002466]
  We propose a framework called Prototypical Residual Network (PRN) PRN learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. We present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies.
  arXiv  Detail & Related papers  (2022-12-05T05:03:46Z)
• Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images [29.57501199670898]
  We introduce one-class semi-supervised learning (OC-SSL) to utilize known normal and unlabeled images for training. Ensembles of reconstruction networks are designed to model the distribution of normal images and the distribution of both normal and unlabeled images. We propose a new perspective on self-supervised learning, which is designed to refine the anomaly scores rather than detect anomalies directly.
  arXiv  Detail & Related papers  (2022-10-09T11:18:45Z)
• Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization [19.23452967227186]
  We propose a novel framework for unsupervised anomaly detection and localization. Our method aims at learning dense and compact distribution from normal images with a coarse-to-fine alignment process. Our framework is effective in detecting various real-world defects and achieves a new state-of-the-art in industrial unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-10-09T10:44:58Z)
• DR{\AE}M -- A discriminatively trained reconstruction embedding for surface anomaly detection [14.234783431842542]
  We propose a discriminatively trained reconstruction anomaly embedding model (DRAEM) DRAEM learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-the-art unsupervised methods by a large margin.
  arXiv  Detail & Related papers  (2021-08-17T13:17:29Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)

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.