Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption

• URL: http://arxiv.org/abs/2303.03925v1
• Date: Thu, 2 Mar 2023 22:59:20 GMT
• Title: Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
• Authors: Jack W Barker, Neelanjan Bhowmik, Yona Falinie A Gaus and Toby P Breckon
• Abstract summary: Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Deep Autoencoders (AE) have been widely used foranomaly detection tasks, but suffer from overfitting to a null identity function. We introduce an efficient methodof producing Adversarially Learned Continuous Noise (ALCN) to maximally globally corrupt the input prior to denoising.
• Score: 11.135527192198092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that models must learn effective rep-resentations solely across normal class data samples. Deep Autoencoders (AE) have been widely used foranomaly detection tasks, but suffer from overfitting to a null identity function. To address this problem, weimplement a training scheme applied to a Denoising Autoencoder (DAE) which introduces an efficient methodof producing Adversarially Learned Continuous Noise (ALCN) to maximally globally corrupt the input priorto denoising. Prior methods have applied similar approaches of adversarial training to increase the robustnessof DAE, however they exhibit limitations such as slow inference speed reducing their real-world applicabilityor producing generalised obfuscation which is more trivial to denoise. We show through rigorous evaluationthat our ALCN method of regularisation during training improves AUC performance during inference whileremaining efficient over both classical, leave-one-out novelty detection tasks with the variations-: 9 (normal)vs. 1 (abnormal) & 1 (normal) vs. 9 (abnormal); MNIST - AUCavg: 0.890 & 0.989, CIFAR-10 - AUCavg: 0.670& 0.742, in addition to challenging real-world anomaly detection tasks: industrial inspection (MVTEC-AD -AUCavg: 0.780) and plant disease detection (Plant Village - AUC: 0.770) when compared to prior approaches.

Related papers

• Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders [101.42201747763178]
  Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Our work provides a novel disentanglement mechanism to build an efficient pre-training purification method.
  arXiv  Detail & Related papers  (2024-05-02T16:49:25Z)
• 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)
• Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detection [5.1308092683559225]
  Unsupervised anomalous sound detection (ASD) aims to identify anomalous sounds by learning the features of normal operational sounds and sensing their deviations. Recent approaches have focused on the self-supervised task utilizing the classification of normal data, and advanced models have shown that securing representation space for anomalous data is important. We propose a training technique aimed at ensuring intra-class compactness and increasing the angle gap between normal and abnormal samples.
  arXiv  Detail & Related papers  (2023-10-10T07:04:36Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Simple Adaptive Projection with Pretrained Features for Anomaly Detection [0.0]
  We propose a novel adaptation framework including simple linear transformation and self-attention. Our simple adaptive projection with pretrained features(SAP2) yields a novel anomaly detection criterion.
  arXiv  Detail & Related papers  (2021-12-05T15:29:59Z)
• Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks [86.42889611784855]
  normalization methods increase the vulnerability with respect to noise and input corruptions. We propose an unsupervised non-parametric distribution correction method that adapts the activation distribution of each layer. In our experiments, we empirically show that the proposed method effectively reduces the impact of intense image corruptions.
  arXiv  Detail & Related papers  (2021-10-05T11:36:25Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)
• Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments [63.942632088208505]
  Inherent to today's operating environment is the practice of adversarial machine learning. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments.
  arXiv  Detail & Related papers  (2021-05-14T10:05:10Z)
• PANDA : Perceptually Aware Neural Detection of Anomalies [20.838700258121197]
  We propose a novel fine-grained VAE-GAN architecture trained in a semi-supervised manner to detect both visually distinct and subtle anomalies. With the use of a residually connected dual-feature extractor, a fine-grained discriminator and a perceptual loss function, we are able to detect subtle, low inter-class (anomaly vs. normal) variant anomalies.
  arXiv  Detail & Related papers  (2021-04-28T11:03:50Z)
• CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection [20.368114998124295]
  We propose a weakly supervised anomaly detection method which has manifold contributions. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively.
  arXiv  Detail & Related papers  (2020-11-24T13:27:40Z)
• Deep Anomaly Detection by Residual Adaptation [34.47788988778933]
  We propose a novel approach to deep anomaly detection based on augmenting large pretrained networks with residual corrections that adjusts them to the task of anomaly detection. Our method gives rise to a highly parameter-efficient learning mechanism, enhances disentanglement of representations in the pretrained model, and outperforms all existing anomaly detection methods.
  arXiv  Detail & Related papers  (2020-10-05T20:02:58Z)

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.