PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies

• URL: http://arxiv.org/abs/2303.10704v1
• Date: Sun, 19 Mar 2023 16:19:13 GMT
• Title: PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies
• Authors: Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
• Abstract summary: Autoencoder (AE) is trained to reconstruct the normal only training data with the expectation that, in test time, it can poorly reconstruct the anomalous data. We propose to limit the anomaly reconstruction capability of AEs by incorporating pseudo anomalies during the training of an AE. We demonstrate the effectiveness of our proposed pseudo anomaly based training approach against several existing state-ofthe-art (SOTA) methods on three benchmark video anomaly datasets.
• Score: 13.14903445595385
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained to reconstruct the normal only training data with the expectation that, in test time, it can poorly reconstruct the anomalous data. However, previous studies have shown that, even trained with only normal data, AEs can often reconstruct anomalous data as well, resulting in a decreased performance. To mitigate this problem, we propose to limit the anomaly reconstruction capability of AEs by incorporating pseudo anomalies during the training of an AE. Extensive experiments using five types of pseudo anomalies show the robustness of our training mechanism towards any kind of pseudo anomaly. Moreover, we demonstrate the effectiveness of our proposed pseudo anomaly based training approach against several existing state-ofthe-art (SOTA) methods on three benchmark video anomaly datasets, outperforming all the other reconstruction-based approaches in two datasets and showing the second best performance in the other dataset.

Related papers

• Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies [17.342474659784823]
  A typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal data. We propose creating pseudo anomalies from learned adaptive noise by exploiting the weakness of AE, i.e., reconstructing anomalies too well.
  arXiv  Detail & Related papers  (2024-05-09T16:22:24Z)
• 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)
• 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)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder [1.9511777443446219]
  We propose a simple yet efficient framework for video anomaly detection. The pseudo anomaly samples are synthesized from only normal data by embedding random mask tokens without extra data processing. We also propose a normalcy consistency training strategy that encourages the AEs to better learn the regular knowledge from normal and corresponding pseudo anomaly data.
  arXiv  Detail & Related papers  (2023-03-09T08:33:38Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection [16.436293069942312]
  Autoencoders (AEs) often start reconstructing anomalies as well which depletes their anomaly detection performance. We propose a temporal pseudo anomaly synthesizer that generates fake-anomalies using only normal data. An AE is then trained to maximize the reconstruction loss on pseudo anomalies while minimizing this loss on normal data.
  arXiv  Detail & Related papers  (2021-10-19T07:08:44Z)
• Learning Not to Reconstruct Anomalies [14.632592282260363]
  Autoencoder (AE) is trained to reconstruct the input with training set consisting only of normal data. AE is then expected to well reconstruct the normal data while poorly reconstructing the anomalous data. We propose a novel methodology to train AEs with the objective of reconstructing only normal data, regardless of the input.
  arXiv  Detail & Related papers  (2021-10-19T05:22:38Z)
• 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)
• 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.