ARCADe: A Rapid Continual Anomaly Detector

• URL: http://arxiv.org/abs/2008.04042v2
• Date: Sun, 18 Oct 2020 17:34:02 GMT
• Title: ARCADe: A Rapid Continual Anomaly Detector
• Authors: Ahmed Frikha, Denis Krompa{\ss} and Volker Tresp
• Abstract summary: We address a novel learning problem of continual anomaly detection (CAD) We propose ARCADe, an approach to train neural networks to be robust against the major challenges of this new learning problem. The results of our experiments on three datasets show that ARCADe substantially outperforms baselines from the continual learning and anomaly detection literature.
• Score: 25.34227775187408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a learning scenario where a model has to incrementally learn a sequence of anomaly detection tasks, i.e. tasks from which only examples from the normal (majority) class are available for training. We define this novel learning problem of continual anomaly detection (CAD) and formulate it as a meta-learning problem. Moreover, we propose A Rapid Continual Anomaly Detector (ARCADe), an approach to train neural networks to be robust against the major challenges of this new learning problem, namely catastrophic forgetting and overfitting to the majority class. The results of our experiments on three datasets show that, in the CAD problem setting, ARCADe substantially outperforms baselines from the continual learning and anomaly detection literature. Finally, we provide deeper insights into the learning strategy yielded by the proposed meta-learning algorithm.

Related papers

• Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study [0.6291443816903801]
  This paper evaluates a diverse array of machine learning-based anomaly detection algorithms. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms.
  arXiv  Detail & Related papers  (2024-02-11T19:12:51Z)
• Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution [89.16750999704969]
  Anomaly labels hinder traditional supervised models in time series anomaly detection. Various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. We propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD)
  arXiv  Detail & Related papers  (2023-11-19T05:37:18Z)
• Precursor-of-Anomaly Detection for Irregular Time Series [31.73234935455713]
  We present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm.
  arXiv  Detail & Related papers  (2023-06-27T14:10:09Z)
• CNTS: Cooperative Network for Time Series [7.356583983200323]
  This paper presents a novel approach for unsupervised anomaly detection called the Cooperative Network Time Series approach. The central aspect of CNTS is a multi-objective optimization problem, which is solved through a cooperative solution strategy. Experiments on three real-world datasets demonstrate the state-of-the-art performance of CNTS and confirm the cooperative effectiveness of the detector and reconstructor.
  arXiv  Detail & Related papers  (2023-02-20T06:55:10Z)
• Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
  We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
  arXiv  Detail & Related papers  (2022-10-27T19:45:12Z)
• Fine-grained Anomaly Detection via Multi-task Self-Supervision [2.9237210794416755]
  Self-supervised learning has greatly helped many methods including anomaly detection. By combining in a multi-task framework high-scale shape features oriented task with low-scale fine features oriented task, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems.
  arXiv  Detail & Related papers  (2021-04-20T14:19:08Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)
• Anomaly Detection in Video via Self-Supervised and Multi-Task Learning [113.81927544121625]
  Anomaly detection in video is a challenging computer vision problem. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level.
  arXiv  Detail & Related papers  (2020-11-15T10:21:28Z)
• 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)
• Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error [50.83356836818667]
  We develop a novel meta-regularization objective using it cyclical annealing schedule and it maximum mean discrepancy (MMD) criterion. The experimental results show that our approach substantially outperforms standard meta-learning algorithms.
  arXiv  Detail & Related papers  (2020-03-04T04:43:16Z)

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.