ARES: Locally Adaptive Reconstruction-based Anomaly Scoring

• URL: http://arxiv.org/abs/2206.07604v1
• Date: Wed, 15 Jun 2022 15:35:12 GMT
• Title: ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
• Authors: Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng
• Abstract summary: We show that anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples. We propose a novel Adaptive Reconstruction Error-based Scoring approach, which adapts its scoring based on the local behaviour of reconstruction error over the latent space.
• Score: 25.707159917988733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How can we detect anomalies: that is, samples that significantly differ from a given set of high-dimensional data, such as images or sensor data? This is a practical problem with numerous applications and is also relevant to the goal of making learning algorithms more robust to unexpected inputs. Autoencoders are a popular approach, partly due to their simplicity and their ability to perform dimension reduction. However, the anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples, which hinders their ability to detect real anomalies. In this paper, we empirically demonstrate the importance of local adaptivity for anomaly scoring in experiments with real data. We then propose our novel Adaptive Reconstruction Error-based Scoring approach, which adapts its scoring based on the local behaviour of reconstruction error over the latent space. We show that this improves anomaly detection performance over relevant baselines in a wide variety of benchmark datasets.

Related papers

• Anomaly Detection via Autoencoder Composite Features and NCE [1.2891210250935148]
  Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE)
  arXiv  Detail & Related papers  (2025-02-04T01:29:22Z)
• Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning [16.296864509584346]
  In this paper, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder's reconstruction ability on anomalies and thus improve the detection accuracy. Although no patch-level anomalous information is available, we rigorously prove that the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones.
  arXiv  Detail & Related papers  (2024-12-17T12:24:08Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• 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)
• AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder [3.31490164885582]
  We propose a vision transformer-based encoder-decoder model, named AnoViT, to reflect normal information by additionally learning the global relationship between image patches. The proposed model performed better than the convolution-based model on three benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-21T09:01:37Z)
• Simple Adaptive Projection with Pretrained Features for Anomaly Detection [0.0]
  We propose a novel adaptation framework including simple linear transformation and self-attention. Our simple adaptive projection with pretrained features(SAP2) yields a novel anomaly detection criterion.
  arXiv  Detail & Related papers  (2021-12-05T15:29:59Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection [9.19194451963411]
  Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. We propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation.
  arXiv  Detail & Related papers  (2021-06-09T21:57:41Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.