History-based Anomaly Detector: an Adversarial Approach to Anomaly Detection

• URL: http://arxiv.org/abs/1912.11843v2
• Date: Sat, 14 Mar 2020 15:41:03 GMT
• Title: History-based Anomaly Detector: an Adversarial Approach to Anomaly Detection
• Authors: Pierrick Chatillon and Coloma Ballester
• Abstract summary: Anomaly detection is a difficult problem in many areas and has recently been subject to a lot of attention. We propose a simple yet new adversarial method to tackle this problem, denoted as History-based anomaly detector (HistoryAD) It consists of a self-supervised model, trained to recognize 'normal' samples by comparing them to samples based on the training history of a previously trained GAN.
• Score: 3.908842679355254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection is a difficult problem in many areas and has recently been subject to a lot of attention. Classifying unseen data as anomalous is a challenging matter. Latest proposed methods rely on Generative Adversarial Networks (GANs) to estimate the normal data distribution, and produce an anomaly score prediction for any given data. In this article, we propose a simple yet new adversarial method to tackle this problem, denoted as History-based anomaly detector (HistoryAD). It consists of a self-supervised model, trained to recognize 'normal' samples by comparing them to samples based on the training history of a previously trained GAN. Quantitative and qualitative results are presented evaluating its performance. We also present a comparison to several state-of-the-art methods for anomaly detection showing that our proposal achieves top-tier results on several datasets.

Related papers

• Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination [20.4008901760593]
  We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end. Our proposed method surpasses competing techniques and exhibits both stability and robustness in the presence of data contamination.
  arXiv  Detail & Related papers  (2024-11-14T16:10:15Z)
• 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)
• 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)
• Few-shot Anomaly Detection in Text with Deviation Learning [13.957106119614213]
  We introduce FATE, a framework that learns anomaly scores explicitly in an end-to-end method using deviation learning. Our model is optimized to learn the distinct behavior of anomalies by utilizing a multi-head self-attention layer and multiple instance learning approaches.
  arXiv  Detail & Related papers  (2023-08-22T20:40:21Z)
• On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection [55.73320979733527]
  We propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs. Experimental results show that our method achieves competitive detection performance on various text classification tasks.
  arXiv  Detail & Related papers  (2023-06-27T02:54:07Z)
• AGAD: Adversarial Generative Anomaly Detection [12.68966318231776]
  Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. We propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm. Our method generates pseudo-anomaly data for both supervised and semi-supervised anomaly detection scenarios.
  arXiv  Detail & Related papers  (2023-04-09T10:40:02Z)
• 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)
• Self-Trained One-class Classification for Unsupervised Anomaly Detection [56.35424872736276]
  Anomaly detection (AD) has various applications across domains, from manufacturing to healthcare. In this work, we focus on unsupervised AD problems whose entire training data are unlabeled and may contain both normal and anomalous samples. To tackle this problem, we build a robust one-class classification framework via data refinement. We show that our method outperforms state-of-the-art one-class classification method by 6.3 AUC and 12.5 average precision.
  arXiv  Detail & Related papers  (2021-06-11T01:36:08Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data [150.9270911031327]
  We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies.
  arXiv  Detail & Related papers  (2020-09-15T03:05:39Z)

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.