Beyond Individual Input for Deep Anomaly Detection on Tabular Data

• URL: http://arxiv.org/abs/2305.15121v6
• Date: Thu, 2 May 2024 14:57:25 GMT
• Title: Beyond Individual Input for Deep Anomaly Detection on Tabular Data
• Authors: Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan,
• Abstract summary: Anomaly detection is vital in many domains, such as finance, healthcare, and cybersecurity. To the best of our knowledge, this is the first work to successfully combine feature-feature and sample-sample dependencies. Our method achieves state-of-the-art performance, outperforming existing methods by 2.4% and 1.2% in terms of F1-score and AUROC, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection is vital in many domains, such as finance, healthcare, and cybersecurity. In this paper, we propose a novel deep anomaly detection method for tabular data that leverages Non-Parametric Transformers (NPTs), a model initially proposed for supervised tasks, to capture both feature-feature and sample-sample dependencies. In a reconstruction-based framework, we train an NPT to reconstruct masked features of normal samples. In a non-parametric fashion, we leverage the whole training set during inference and use the model's ability to reconstruct the masked features to generate an anomaly score. To the best of our knowledge, this is the first work to successfully combine feature-feature and sample-sample dependencies for anomaly detection on tabular datasets. Through extensive experiments on 31 benchmark tabular datasets, we demonstrate that our method achieves state-of-the-art performance, outperforming existing methods by 2.4% and 1.2% in terms of F1-score and AUROC, respectively. Our ablation study further proves that modeling both types of dependencies is crucial for anomaly detection on tabular data.

Related papers

• MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection [0.0]
  A new meth-odology for detecting surface defects in in-dustrial settings is introduced, referred to as Memory Augmentation and Pseudo-Labeling(MAPL) The methodology first in-troduces an anomaly simulation strategy, which significantly improves the model's ability to recognize rare or unknown anom-aly types. An end-to-end learning framework is employed by MAPL to identify the abnormal regions directly from the input data.
  arXiv  Detail & Related papers  (2024-05-10T02:26:35Z)
• Retrieval Augmented Deep Anomaly Detection for Tabular Data [0.0]
  Research has introduced retrieval-augmented models to address this gap. We propose a reconstruction-based approach in which a transformer model learns to reconstruct masked features of textitnormal samples. Experiments on a benchmark of 31 datasets reveal that augmenting this reconstruction-based anomaly detection method with sample-sample dependencies via retrieval modules significantly boosts performance.
  arXiv  Detail & Related papers  (2024-01-30T14:33:18Z)
• 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)
• MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection [6.16984478518058]
  MEMTO is a memory-guided Transformer that learns the degree to which each memory item should be updated in response to the input data. We evaluate our proposed method on five real-world datasets from diverse domains.
  arXiv  Detail & Related papers  (2023-12-05T06:28:19Z)
• 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)
• Active anomaly detection based on deep one-class classification [9.904380236739398]
  We tackle two essential problems of active learning for Deep SVDD: query strategy and semi-supervised learning method. First, rather than solely identifying anomalies, our query strategy selects uncertain samples according to an adaptive boundary. Second, we apply noise contrastive estimation in training a one-class classification model to incorporate both labeled normal and abnormal data effectively.
  arXiv  Detail & Related papers  (2023-09-18T03:56:45Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection [54.76993389109327]
  Unsupervised anomaly detection aims to build models to detect unseen anomalies by only training on the normal data. We propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges. AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations.
  arXiv  Detail & Related papers  (2022-01-03T03:40:21Z)
• 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)
• Out-Of-Bag Anomaly Detection [0.9449650062296822]
  Data anomalies are ubiquitous in real world datasets, and can have an adverse impact on machine learning (ML) systems. We propose a novel model-based anomaly detection method, that we call Out-of-Bag anomaly detection. We show our method can improve the accuracy and reliability of an ML system as data pre-processing step via a case study on home valuation.
  arXiv  Detail & Related papers  (2020-09-20T06:01:52Z)

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.