Backpropagated Gradient Representations for Anomaly Detection

• URL: http://arxiv.org/abs/2007.09507v1
• Date: Sat, 18 Jul 2020 19:39:42 GMT
• Title: Backpropagated Gradient Representations for Anomaly Detection
• Authors: Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib
• Abstract summary: Anomalies require more drastic model updates to fully represent them compared to normal data. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets.
• Score: 19.191613437266184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal data. Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.

Related papers

• 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)
• Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [11.245813423781415]
  We devise novel data-driven supervision for data by introducing a characteristic -- scale -- as data labels. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method.
  arXiv  Detail & Related papers  (2023-05-25T14:48:00Z)
• CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization [2.8659934481869715]
  Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring anomalous data during training. Most current state-of-the-art methods use latent variable generative models operating directly on the images. We propose CRADL whose core idea is to model the distribution of normal samples directly in the low-dimensional representation space of an encoder trained with a contrastive pretext-task.
  arXiv  Detail & Related papers  (2023-01-05T16:07:49Z)
• Framing Algorithmic Recourse for Anomaly Detection [18.347886926848563]
  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)
  arXiv  Detail & Related papers  (2022-06-29T03:30:51Z)
• 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)
• Gradient-Based Adversarial and Out-of-Distribution Detection [15.510581400494207]
  We introduce confounding labels in gradient generation to probe the effective expressivity of neural networks. We show that our gradient-based approach allows for capturing the anomaly in inputs based on the effective expressivity of the models.
  arXiv  Detail & Related papers  (2022-06-16T15:50:41Z)
• 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)
• 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)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• 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.