VAE-based Deep SVDD for anomaly detection

Zhou, Yu; Liang, Xiaomin; Zhang, Wei; Zhang, Linrang; Song, Xing

doi:10.1016/j.neucom.2021.04.089

Cited by 72 publications

(23 citation statements)

References 18 publications

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“…It has also been shown that VAE models are capable of learning representations with disentangled factors (Higgins et al, 2016a) due to the isotropic Gaussian priors on the latent variable, the known power of the Bayesian models. The better performance of VAE compared to AE models has also been shown previously in other applications such as anomaly detection, object identification, and BCIs (Dai et al, 2019;Tahir et al, 2021;Zhou et al, 2021). As an additional point, comparing the results of VAE with SVAE, suggests the added value of supervised learning in training better models.…”

Section: Impact Of Variational Inference In Feature Learningsupporting

confidence: 72%

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Salsabilian

Najafizadeh

2022

Front. Signal Process.

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Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification models to generalize to data from unseen subjects. To tackle this problem, we present a long short-term memory-based adversarial variational autoencoder (LSTM-AVAE) framework for subject-invariant mTBI feature extraction. In the proposed model, first, an LSTM variational autoencoder (LSTM-VAE) combines the representation learning ability of the variational autoencoder (VAE) with the temporal modeling characteristics of the LSTM to learn the latent space representations from neural activity. Then, to detach the subject’s individuality from neural feature representations, and make the model proper for cross-subject transfer learning, an adversary network is attached to the encoder in a discriminative setting. The model is trained using the 1 held-out approach. The trained encoder is then used to extract the representations from the held-out subject’s data. The extracted representations are then classified into normal and mTBI groups using different classifiers. The proposed model is evaluated on cortical recordings of Thy1-GCaMP6s transgenic mice obtained via widefield calcium imaging, prior to and after inducing injury. In cross-subject transfer learning experiment, the proposed LSTM-AVAE framework achieves classification accuracy results of 95.8% and 97.79%, without and with utilizing conditional VAE (cVAE), respectively, demonstrating that the proposed model is capable of learning invariant representations from mTBI data.

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Section: Impact Of Variational Inference In Feature Learningsupporting

confidence: 72%

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Salsabilian

Najafizadeh

2022

Front. Signal Process.

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“…This aspect involves several weaknesses in using an AE for anomaly detection tasks rather than a VAE, whom probabilistic encoder models the distribution parameters of the latent variables rather than the latent variables theirselves [14], thus capturing more data variability and resulting in a more homogeneous latent space than a standard AE. In [48], a general anomaly detection method based on VAE and Support Vector Data Description (SVDD) [49] is proposed where the SVDD decision boundary is simultaneously learnt with the latent representations of data and fitted on them, in order to avoid the hypersphere collapse, i.e. the mapping of all data to a single point in latent space [50].…”

Section: Related Workmentioning

confidence: 99%

“…Leveraging on this formulation, VAE training can be performed by maximizing the ELBO [48]. However, the expected reconstruction error requires the sampling of random latent variables z from the approximated posterior q φ (z|x), which makes the training intractable in practice since the gradient of the ELBO with respect to the parameters φ can not be estimated.…”

Section: Variational Autoencodermentioning

confidence: 99%

Unsupervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

Pollastro¹,

Testa²,

Bilotta³

et al. 2022

Preprint

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In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods, whom performances are maximised using hyperparameter optimization techniques. The paper proposes a semi-supervised method with a datadriven approach to detect structural anomalies. The methodology consists of: (i) a Variational Autoencoder (VAE) to approximate undamaged data distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage sensitive features extracted from VAE's signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage's scenarios by IASC-ASCE Structural Health Monitoring Task Group.

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“…The article [15] presents a deep support vector data description based on a variational autoencoder (Deep SVDD-VAE). In the proposed model, the VAE is used to reconstruct the input instances, while a spherical discriminative boundary is learned with the latent representations simultaneously based on the SVDD.…”

Section: Similar Solutionsmentioning

confidence: 99%

Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder

Gabryel

Lada²,

Filutowicz

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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This paper presents a neural network model for identifying non-human traffic to a web-site, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.

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VAE-based Deep SVDD for anomaly detection

Cited by 72 publications

References 18 publications

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Unsupervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder

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