Framing Algorithmic Recourse for Anomaly Detection

• URL: http://arxiv.org/abs/2206.14384v1
• Date: Wed, 29 Jun 2022 03:30:51 GMT
• Title: Framing Algorithmic Recourse for Anomaly Detection
• Authors: Debanjan Datta, Feng Chen, Naren Ramakrishnan
• Abstract summary: We present an approach -- Context preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT) CARAT uses a transformer based encoder-decoder model to explain an anomaly by finding features with low likelihood. Semantically coherent counterfactuals are generated by modifying the highlighted features, using the overall context of features in the anomalous instance(s)
• Score: 18.347886926848563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The problem of algorithmic recourse has been explored for supervised machine learning models, to provide more interpretable, transparent and robust outcomes from decision support systems. An unexplored area is that of algorithmic recourse for anomaly detection, specifically for tabular data with only discrete feature values. Here the problem is to present a set of counterfactuals that are deemed normal by the underlying anomaly detection model so that applications can utilize this information for explanation purposes or to recommend countermeasures. We present an approach -- Context preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT), that is effective, scalable, and agnostic to the underlying anomaly detection model. CARAT uses a transformer based encoder-decoder model to explain an anomaly by finding features with low likelihood. Subsequently semantically coherent counterfactuals are generated by modifying the highlighted features, using the overall context of features in the anomalous instance(s). Extensive experiments help demonstrate the efficacy of CARAT.

Related papers

• MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring [2.394081903745099]
  We propose MeLIAD, a novel methodology for interpretable anomaly detection. MeLIAD is based on metric learning and achieves interpretability by design without relying on any prior distribution assumptions of true anomalies. Experiments on five public benchmark datasets, including quantitative and qualitative evaluation of interpretability, demonstrate that MeLIAD achieves improved anomaly detection and localization performance.
  arXiv  Detail & Related papers  (2024-09-20T16:01:43Z)
• Can I trust my anomaly detection system? A case study based on explainable AI [0.4416503115535552]
  This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences.
  arXiv  Detail & Related papers  (2024-07-29T12:39:07Z)
• Unsupervised Anomaly Detection Using Diffusion Trend Analysis [48.19821513256158]
  We propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation. The proposed method is validated on an open dataset for industrial anomaly detection.
  arXiv  Detail & Related papers  (2024-07-12T01:50:07Z)
• 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)
• 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)
• 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)
• 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)
• Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation [33.31923133201812]
  This paper introduces Dependency-based Anomaly Detection (DepAD) DepAD reframes unsupervised anomaly detection as supervised feature selection and prediction tasks. Two DepAD algorithms emerge as all-rounders and superior performers in handling a wide range of datasets.
  arXiv  Detail & Related papers  (2020-11-13T01:39:44Z)
• A Transfer Learning Framework for Anomaly Detection Using Model of Normality [2.9685635948299995]
  Convolutional Neural Network (CNN) techniques have proven to be very useful in image-based anomaly detection applications. We introduce a transfer learning framework for anomaly detection based on similarity measure with a Model of Normality (MoN) We show that with the proposed threshold settings, a significant performance improvement can be achieved.
  arXiv  Detail & Related papers  (2020-11-12T05:26:32Z)
• Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection [5.491655566898372]
  We build a scalable machine learning system for unsupervised anomaly detection via representation learning. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error. We show empirically the competitive performance of our approach on benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-05T00:03:48Z)
• 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.