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Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [article]

Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi
2021 arXiv   pre-print
First, we propose Adversarial Mirrored Autoencoder (AMA), a variant of Adversarial Autoencoder, which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction  ...  Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.  ...  Conclusion In this paper, we have we introduced a new method for the unsupervised anomaly detection problem, Adversarial Mirrored Autoencoder (AMA), equipped with Mirrored Wasserstein loss and a latent  ... 
arXiv:2003.10713v3 fatcat:rhff735uxrborke4rwisjs6wm4

Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays [article]

Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
2021 arXiv   pre-print
To solve this problem, we present several unsupervised deep learning approaches, including convolutional and adversarially trained autoencoder.  ...  19 as an anomaly.  ...  Unsupervised Deep Learning based Anomaly Detection For detecting anomalies in CXR images, we investigate two types of unsupervised deep learning approaches: convolutional autoencoder (CAE) 1 and adversarially  ... 
arXiv:2010.02814v2 fatcat:vwumcbf7qjf2xjva34stoeprlm

Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays

Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
2021 Proceedings of the Canadian Conference on Artificial Intelligence  
To solve this problem, we present several unsupervised deep learning approaches, including convolutional and adversarially trained autoencoder.  ...  -19 as an anomaly.  ...  Unsupervised Deep Learning based Anomaly Detection For detecting anomalies in CXR images, we investigate two types of unsupervised deep learning approaches: convolutional autoencoder (CAE) 1 and adversarially  ... 
doi:10.21428/594757db.fab70f8a fatcat:huceiiodbfexrint663kx7qmne

LPRNet: A Novel Approach for Novelty Detection in Networking Packets

Anshumaan Chauhan, Ayushi Agarwal, Angel Arul Jothi, Sangili Vadivel
2022 International Journal of Advanced Computer Science and Applications  
Recently, this task has been performed using Deep Learning Autoencoders, but they face several drawbacks which include the problem of identity mapping, adversarial perturbations and optimization algorithms  ...  Novelty Detection is a task of recognition of abnormal data points within a given system.  ...  [9] used an adversarial autoencoder with a probabilistic approach for solving the novelty detection problem.  ... 
doi:10.14569/ijacsa.2022.0130213 fatcat:wpwe3khyirfxzl37drhpwlpose

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients [article]

David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein
2019 arXiv   pre-print
In our experiments, Variational Autoencoder gradient-based rating outperforms other approaches on unsupervised pixel-wise tumor detection on the BraTS-2017 dataset with a ROC-AUC of 0.94.  ...  Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error.  ...  Previous unsupervised anomaly detection approaches in the medical field were primarily based on a reconstruction error. Leemput et al.  ... 
arXiv:1912.00003v1 fatcat:4xjvurdwvvbwxfd52oarayofey

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection [article]

David Zimmerer, Simon A. A. Kohl, Jens Petersen, Fabian Isensee, Klaus H. Maier-Hein
2018 arXiv   pre-print
We address these shortcomings by proposing the Context-encoding Variational Autoencoder (ceVAE) which combines reconstruction- with density-based anomaly scoring.  ...  In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images.  ...  Discussion & Conclusion In this work we present ceVAE for unsupervised anomaly detection, combining CEs with VAEs for unsupervised training and detection as well as localization of anomalies in medical  ... 
arXiv:1812.05941v1 fatcat:xnellrlzo5g6tdnj3nhr5jzgva

Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder [article]

Dinesh Jackson Samuel, Fabio Cuzzolin
2021 arXiv   pre-print
In this work we thus propose an unsupervised approach to anomaly detection for robotic-assisted surgery based on deep residual autoencoders.  ...  The end-to-end system was developed and deployed as part of the SARAS demonstration platform for real-time anomaly detection with a processing time of about 25 ms per frame.  ...  Here we propose in particular to tackle anomaly detection in an unsupervised approach based on deep residual autoencoders.  ... 
arXiv:2104.11008v1 fatcat:bvpmpb7gjjfzzg35dixagf7b2m

Ensemble neuroevolution based approach for multivariate time series anomaly detection [article]

Kamil Faber, Dominik Żurek, Marcin Pietroń, Kamil Piętak
2021 arXiv   pre-print
Mostly, they are autoencoder-based architectures with some generative adversarial elements.  ...  The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans.  ...  A wide variety of autoencoders are used, such as variational, denoise or adversarial autoencoders.  ... 
arXiv:2108.03585v1 fatcat:br4t5gha65h4xor75uwpay73b4

ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation [article]

Raunak Dey, Yi Hong
2021 arXiv   pre-print
This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep learning methods and adversarial-based anomaly/novelty detection algorithms.  ...  This concept tackles the task of unsupervised anomaly segmentation, which has attracted increasing attention in recent years due to their broad applications in tasks with unlabelled data.  ...  works on anomaly detection directly.  ... 
arXiv:2103.03664v2 fatcat:3mzltezwubfqfkqv5fkqzburam

Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds [article]

Alexandrine Ribeiro, Luis Miguel Matos, Pedro Jose Pereira, Eduardo C. Nunes, Andre L. Ferreira, Paulo Cortez, Andre Pilastri
2020 arXiv   pre-print
The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process.  ...  The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features.  ...  Thus, the magnitude of the reconstruction error can be used to detect anomalies. The proposed unsupervised anomaly detection approaches consist of simple AE networks.  ... 
arXiv:2006.10417v2 fatcat:clqtupdyh5g35am6sqzttagqxu

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Kamil Faber, Marcin Pietron, Dominik Zurek
2021 Entropy  
Mostly, they are autoencoder-based architectures with some generative adversarial elements.  ...  The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans.  ...  A wide variety of autoencoders are used, such as variational, denoising, and adversarial autoencoders.  ... 
doi:10.3390/e23111466 pmid:34828164 pmcid:PMC8621716 fatcat:nsdsrpnujvcddaxpc7huebbwzy

ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network [article]

Raunak Dey, Wenbo Sun, Haibo Xu, Yi Hong
2021 arXiv   pre-print
This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep segmentation and adversarial-based anomaly/novelty detection algorithms.  ...  We introduce a segmentation network that utilizes adversarial learning to partition an image into two cuts, with one of them falling into a reference distribution provided by the user.  ...  Following the success of AnoGAN idea of anomaly detection and accompanied by advances in Variational Autoencoders (VAE) [21, 22] , adversarial autoencoders in [25] replace the KL-divergence loss in  ... 
arXiv:2112.09135v1 fatcat:gqnklo7kojbrlbz5navgn7slgq

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
End-to-End Optimized Compressio for Brain Images Zhang, Yuge CNN-BASED ANOMALY DETECTION FOR FACE PRESENTATION ATTACK DETECTION WITH MULTI-CHANNEL IMAGES Zhang, Yuhang Video Super Resolution Using Temporal  ...  CNN-BASED ANOMALY DETECTION FOR FACE PRESENTATION ATTACK DETECTION WITH MULTI-CHANNEL IMAGES Yan, Longbin A MULTI-MODEL FUSION FRAMEWORK FO NIR-TO-RGB TRANSLATION Yan, Ning Chain Code-Based  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

Anomaly Detection using Deep Learning based Image Completion [article]

Matthias Haselmann, Dieter P. Gruber, Paul Tabatabai
2018 arXiv   pre-print
The pixel-wise reconstruction error within the cut out region is an anomaly image which can be used for anomaly detection.  ...  Results on surface images of decorated plastic parts demonstrate that this approach is suitable for detection of visible anomalies and moreover surpasses all other tested methods.  ...  Competence Center Leoben GmbH (PCCL, Austria) within the framework of the COMET-program of the Federal Ministry for Transport, Innovation and Technology and the Federal Ministry of Economy, Family and Youth with  ... 
arXiv:1811.06861v1 fatcat:oak3akf57zgrxejjnmjoj4awqi

Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning [article]

Behzad Bozorgtabar, Dwarikanath Mahapatra, Guillaume Vray, Jean-Philippe Thiran
2020 arXiv   pre-print
Hence, training anomaly detection in a fully unsupervised or self-supervised fashion would be advantageous, allowing a significant reduction of time spent on the report by radiologists.  ...  In this paper, we present SALAD, an end-to-end deep self-supervised methodology for anomaly detection on X-Ray images.  ...  New anomaly detection methods [23, 9, 25] built upon generative adversarial networks (GANs) [5, 6, 13] have shown promising anomaly detection performance by using GANs' ability to learn a manifold  ... 
arXiv:2010.09856v1 fatcat:w7f7lvvphze2ldx6q4ub533edm
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