Anomaly detection with conditional variational autoencoders Nov 13, 2025 · Conditional Variational Autoencoders (CVAE) take this concept a step further by allowing us to generate data conditional on some input variables. At its core, the quantized gated recurrent unit variational autoencoder (Q-GRU-VAE) architecture, a significant evolution from conventional methods, offers an extremely lightweight yet highly effective solution. In Nov 30, 2023 · To realize the anomaly detection for industrial multi-sensor data, we develop a novel multi-scale convolutional recurrent variational autoencoder (MSCRVAE) model. These techniques help ensure that anomalous nodes are detected more accurately despite the inherent challenges of federated graph systems. Previous works argued that training VAE models only with inliers is insufficient and the frame Jun 20, 2025 · These deep neural networks are great for tasks like noise reduction, anomaly detection, and feature extraction because they can reflect the most important parts of a dataset by compressing the raw input data. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. A generative model can learn a probability distribution model by being trained on an anomaly-free dataset. You can use different variations of VAEs for different purposes Oct 12, 2020 · Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Jan 1, 2024 · Thus, an anomaly detection system could further ensure the stability and reliability of the energy supply of the DPV system and promote its massive deployment. Mar 11, 2024 · Therefore, anomaly detection in smart meter data plays an important role in ensuring the healthy operation of an energy system. Mar 15, 2025 · Autoencoders have become a fundamental technique in deep learning (DL), significantly enhancing representation learning across various domains, including image processing, anomaly detection, and generative modelling. Existing approaches such as deep autoencoder (DAE), variational autoencoder (VAE), conditional… Mar 31, 2025 · What is a Variational Autoencoder? Variational Autoencoders (VAEs) are a type of artificial neural network architecture that combines the power of autoencoders with probabilistic methods. Therefore, there is a great demand for a highly accurate and efficient automatic anomaly detection method based on system log analysis to ensure the Oct 25, 2022 · We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). They are used for generative modeling, meaning they can generate new data samples similar to the training data. The model is a combination of graph neural network layers in a variational Oct 12, 2022 · Anomaly detection is a hot and practical problem. Oct 23, 2024 · The applications of variational autoencoders in technical diagnostics are being developed alongside large-scale studies on transformer models. In this work, we inspect the abilities of conditional variational autoencoders for the unsupervised detection of pathologies. Jul 3, 2019 · Anomaly detection is a very worthwhile question. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Previous works argued that training VAE models only with inliers is insufficient and the frame-work should be significantly modified in order to discriminate the anomalous instances. g. However, traditional autoencoders often grapple with rigid structures that limit their ability to capture intricate nuances and generate diverse outputs. They work by compressing data down to its most fundamental components before rebuilding it with average values, creating something that looks unique but similar to the original data. Oct 12, 2020 · Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Over the time, different variants of autoencoders have been evolved to address the limitations of traditional autoencoder models. Anomaly Detection (AD) is the task of detecting anomalous data points that significantly deviate from expected normal samples [7]. We will try to keep the repository up-to-date and welcome contributions of others when a new matching paper is published or has completed peer-review. Maxime Turgeon and highlights advancements in unsupervised anomaly detection using disentangled learning and conditional variational autoencoders. aly detection (AD) tasks remains an open research question. 2Laboratoire de Recherche en Informatique (LRI) Université Paris-Saclay, Orsay, France Abstract—Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au- toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. [12 - 15] The CVAE architecture blends together several notable techniques that have been developed over several decades. This guide will provide a hands-on approach to building and training a Variational Autoencoder for anomaly detection using Tensor Flow. The most famous deep generative models are variational autoencoders (VAEs) [11] and generative adversarial Nov 3, 2016 · Anomaly detection involves identifying the events which do not conform to an expected pattern in data. Different models Apr 9, 2021 · To solve the mentioned issue, we propose a combined architecture comprising a Conditional Variational AutoEncoder (CVAE) and a Random Forest (RF) classifier to automatically learn similarity among input features, provide a data distribution in order to extract discriminative features from the original features and finally classify various types May 13, 2024 · Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. In this work, we Aug 13, 2024 · Explore Variational Autoencoders (VAEs) in this comprehensive guide. Specifically, autoencoders learn representations of high-dimensional data, and their reconstruction ability can be used to assess whether a new instance is likely to be anomalous. In this work, we use the deep conditional varia- tional autoencoders (CVAE), and we define an original loss function together with a metric that targets AD for hierarchically structured data. However, the anomaly is not a simple two-category in reality, so it is difficult to give accurate results through the comparison of similarities. This repository provides code, explanations, and links to research papers for each model. In this work, we Aug 1, 2024 · This study introduces an unsupervised anomaly detection model called Variational AutoeEncoder with Adversarial Training for Multivariate Time Series Anomaly Detection (VAEAT). Anomaly Detection With Conditional Variational Autoencoders Adrian Alan Pol, Victor Berger, Gianluca Cerminara, Cécile Germain, Maurizio Pierini. We show that, under some assumptions, the VAE-GRF largely outperforms the traditional VAE and some other probabilistic models developed for Abstract Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the gen-erative model with auxiliary covariates. However, with the increasing scale and complexity of distributed systems, the efficiency and accuracy of manual anomaly detection in system logs have decreased. 1651-1657, 10. In Jul 1, 2024 · Effective anomaly detection in multivariate time series (MTS) is very essential for modern complex physical equipment. Apr 22, 2025 · In addition, conditional variational autoencoders (CVAE) are employed alongside contrastive learning strategies to alleviate class imbalance and achieve effective feature disentanglement. DC-VAE detects anomalies in MTS data through a single model, exploiting temporal and spatial MTS information. Variational Autoencoders In many real-world issues, the underlying factor or the underlying data processing may be far easier in a much lower dimensional space than Nov 3, 2021 · Variational AutoEncoders What is it? Variational autoencoder addresses the issue of non-regularized latent space in autoencoder and provides the generative capability to the entire space. In Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. The paper presents a new model to address these challenges. Exploiting the rapid advances in probabilistic inference, in particular variational autoencoders (VAEs) for machine learning (ML) anomaly detection (AD) tasks, remains an open research question. Overall, this paper aims to provide a comprehensive understanding of VAEs, from their fundamental concepts to their practical applications, showcasing their versatility and potential for future research and development. In Part I, we motivated the use of variational autoencoders for With this evidence in mind, we investigated likelihood-based OOD detection for medical images using variational autoencoders (VAEs) [9], an approach that has been taken by [26]. This work exploits the deep conditional variational autoencoder (CVAE) and defines an original loss function together with a metric that targets hierarchically structured data AD and shows the superior performance of this method for classical machine learning (ML) benchmarks and for the application. Jan 1, 2025 · LT-CVAE-GAN synergistically combines Conditional Variational Autoencoders (CVAE) and Conditional Generative Adversarial Networks (CGAN) to exploit the strengths of both frameworks: CVAE’s capabilities in conditional generation and uncertainty management, alongside CGAN’s proficiency in generating high-fidelity samples. Pytorch/TF1 implementation of Variational AutoEncoder for anomaly detection following the paper Variational Autoencoder based Anomaly Detection using Reconstruction Probability by Jinwon An, Sungzoon Cho Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au-toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Feb 12, 2018 · Abstract page for arXiv paper 1802. Utilizing the robust and versatile PyTorch library, this project showcases a straightforward yet effective approach to conditional generative modeling. In this work, we use the deep conditional varia-tional autoencoders (CVAE), and we define an original loss function together with a metric that targets AD for hierarchically structured data. However, most of existing FL approaches are based on supervised techniques, which could require resource-intensive activities and human intervention to obtain labelled datasets. ICMLA 2019 - 18th IEEE International Conference on Machine Learning and Applications, Dec 2019, Boca Raton, United States. Abstract: Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Robust and Unsupervised KPI Anomaly Detection Based on Highly Sensitive Conditional Variational Auto-Encoders Shili Yan†, Bing Tang†, Qing Yang∗, Yijia He‡, Xiaoyuan Zhang† What is a Variational Autoencoder (VAE)? Variational Autoencoders (VAEs) are a powerful type of neural network and a generative model that extends traditional autoencoders by learning a probabilistic representation of data. These techniques mitigate over tting and have nice potential for data model generalization. Different models have to be May 21, 2024 · As the field of anomaly detection with Variational Autoencoders continues to evolve. In this work, we In the recent years and in particular for imaging applica-tions, the dual structure of Variational Autoencoders (VAE) show promising results on data compression or reconstruc-tion. More precisely, we introduce a VAE model with a Gaussian Random Field (GRF) prior, namely VAE-GRF, which generalizes the classical VAE model. The latter describes the phenomena of a posterior that strongly approximates the prior distribution without considering the underlying structure of the data. Our target Oct 12, 2020 · Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Normative modelling has become a popular method for studying such cohorts nlp opencv natural-language-processing deep-learning sentiment-analysis word2vec keras generative-adversarial-network autoencoder glove t-sne segnet keras-models keras-layer latent-dirichlet-allocation denoising-autoencoders svm-classifier resnet-50 anomaly-detection variational-autoencoder Updated on Dec 6, 2021 Python Aug 24, 2024 · This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. Kingma and Max Welling in 2013. In Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Feb 1, 2023 · In this paper, we propose a novel architecture of generative autoencoder by combining the frameworks of $\beta$-VAE, conditional variational autoencoder (CVAE), and the principle of total correlation (TC). A collection of generative AI model implementations, including Variational Autoencoders (VAE), Conditional VAEs, Generative Adversarial Networks (GANs), CycleGAN, Diffusion Models, and Autoencoder-based classification. Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods. In this paper, we design an unsupervised deep learning Abstract—Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au-toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. This approach preserves the disentangling capabilities of the Variational Autoencoder (VAE) while simultaneously performing latent Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Abstract— This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. Previous works argued t… Dec 1, 2019 · Request PDF | On Dec 1, 2019, Adrian Alan Pol and others published Anomaly Detection with Conditional Variational Autoencoders | Find, read and cite all the research you need on ResearchGate Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Although a relatively recent technique, [11] the use of variational autoencoders (VAEs) has since become a common method for anomaly detection. Apr 7, 2025 · Variational Autoencoders offer a powerful, flexible, and unsupervised approach to anomaly detection in time series data. Variational autoencoders (VAEs) are more recent [2], and many applications are still being discovered to this day. Apr 9, 2021 · To solve the mentioned challenges, we propose a combined architecture comprising a Conditional Variational AutoEncoder (CVAE) and a Random Forest (RF) classifier to automatically learn similarity among input features, provide data distribution in order to extract discriminative features from original features, and finally classify various types Jan 14, 2025 · Anomaly detection (AD) plays a pivotal role in AI applications, e. In this paper, we propose an unsupervised model-based In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Anomaly detection techniques, also known as outlier detection, aim to find anomalous events and anomalies that usually do not occur according to historical data samples [15]. Learn VAE architecture, math, and applications in this 2025 guide. They have been successfully applied to anomaly detection tasks, in both supervised [12], [13] and unsupervised [8], [9] settings. This proposed variational autoencoder (VAE) improves latent space sep-aration by conditioning on information within the data. However, like other anomaly detection models, they often struggle to capture effectively both global periodic features and local transient characteristics in an industrial context. Latent space disentanglement is another important area of study with VAEs and has seen much recent progress [14, 19, 46]. These networks utilize an attention mechanism that allows for global feature selection based on long-term dependencies [ Jun 5, 2025 · Anomaly detection in graph-based data is an emerging field in machine learning with many relevant applications. They shine in complex systems where patterns are hard to define and labeling anomalies is impractical. Feb 26, 2024 · Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Nov 28, 2023 · In this work, we propose an anomaly detection framework based on conditional variational autoencoders and Chamfer loss to address the first issue and propose a novel architecture called CLIP-VAE that is tailored to future deployment to online data processing systems. 03903: Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications Apr 25, 2025 · This work presents a novel solution for multivariate time series anomaly detection in industrial control systems (ICSs), specifically tailored for resource-constrained environments. Jiacheng Xu and Greg Durrett. Oct 23, 2024 · Variational autoencoders can face optimization issues such as mode collapse and posterior collapse. have limited representation capabilities in the latent space and, hence, poor anomaly detection perfor-mance. Its fundamental concept involves adopting a two-phase training strategy to improve anomaly detection precision through adversarial reconstruction of raw data. We showcase DC-VAE in different MTS datasets, and portray its future application in a continual Jul 30, 2024 · Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. The architectural configurations under consideration encompass the original VAE baseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE This work uses the deep conditional varia-tional autoencoders (CVAE), and defines an original loss function together with a metric that targets AD for hierarchically structured data that shows the superior performance of this method over vanilla VAEs. Oct 1, 2023 · One of the challenges of studying common neurological disorders is disease heterogeneity including differences in causes, neuroimaging characteristics, comorbidities, or genetic variation. 03903: Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications Time series Anomaly Detection (AD) plays a crucial role for web systems. This makes them particularly useful in tasks such as image generation, data augmentation, and anomaly detection. Jun 27, 2025 · Aiming at the problem of low efficiency and insufficient accuracy of existing anomaly detection methods, this paper proposes a new anomaly detection model based on autoencoder and second-order optimized core vector machine (CVM_2o). We propose a novel neural network model called Multiple-Input Auto-Encoder for AD (MIAEAD) to address However, achieving interpretable, likelihood-compatible compression and fast inference under weak model assumptions remains challenging. Sensors 19, 11 (2019), 2528. 2019. arXiv preprint arXiv:1808. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational We propose a new model of Variational Autoencoder (VAE) for Anomaly Detection (AD) with improved modeling power. We propose a novel Conditional Latent space Variational Autoencoder (CL-VAE) to perform improved pre-processing for anomaly detection on data with known inlier classes and unknown outlier classes. The architectural configurations under consideration encompass the original VAE baseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE Abstract—Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au-toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra Tian-Yang Sun , Tian-Nuo Li. Previous works argued that training VAE models only with inliers is insufficient and the frame Jan 18, 2023 · In essence, defect detection is transformed into an anomaly detection problem. The ori-ginal CVAE model assumes that the data samples are independent, whereas more recent conditional VAE models, such as the Gaussian process (GP) prior VAEs, can account for complex correlation Nov 16, 2024 · The paper is authored by Asif Ahmed Neloy and Dr. Jan 15, 2023 · Comparison of AutoEncoders vs. However, the primary challenge Oct 31, 2025 · Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra Tian-Yang Sun, Tian-Nuo Li, He Wang, Jing-Fei Zhang, Xin Zhang May 13, 2025 · Explore Variational Autoencoders (VAEs), powerful generative models for data creation, anomaly detection, & denoising. It is a hybrid of convolutional autoencoder and convolutional long short-term memory with variational autoencoder (ConvLSTM-VAE). This paper combines Dynamic Bayesian Networks (DBNs) and Neural Networks (NNs) and proposes a method for detecting anomalies in video data at different abstraction levels Recently, generative models have shown promising performance in anomaly detection tasks. Feb 5, 2024 · Time series Anomaly Detection (AD) plays a crucial role for web systems. Jun 11, 2025 · We will mainly focus on Conditional Variational Autoencoders or CVAEs, these are like the next level of AI artistry, merging the strengths of Variational Autoencoders (VAEs) with the ability to follow specific instructions, giving us fine-tuned control over image creation. 1109/ICMLA. A fully unsupervised approach to anomaly detection based on Convolutional Neural Networks and Variational Autoencoders. Abstract—This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic ma-chine monitoring. Anomaly Detection With Conditional Variational Autoencoders. , in classification, and intrusion/threat detection in cybersecurity. Dec 27, 2023 · What are Variational Autoencoders (VAEs)? Autoencoders are ingenious, unsupervised learning mechanisms capable of learning efficient data representations. Aug 24, 2024 · This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. Learning structured output representation using deep conditional generative models. Variational autoencoder (VAE) is a recently-developed deep generative model which has established itself as a powerful method Nov 28, 2022 · Anomaly detection is one of the most widespread use cases for unsupervised machine learning, especially in industrial applications. Although some algorithms have been developed, current models lack consistency on real-world data and often have problems with overfitting. A single anomaly in physical equipment can cause a series of failures due to fault propagation. pp. In this work, we Oct 12, 2020 · Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. This approach is pivotal in domains such as data centers, sensor networks, and finance. The method fits a unique prior distribution to each class in Anomaly detection is an unsupervised pattern recognition task that can be defined under different statistical models. Learn their theoretical concept, architecture, applications, and implementation with PyTorch. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). This work introduces a novel framework for real-time anomaly detection in seasonal time series, with a practical implementation using Conditional Variational Autoencoders based on Multilayer Perceptrons. For instance, PCA has been successfully used for anomaly detection. In this work, we VAEs are a subset of the larger category of autoencoders, a neural network architecture typically used in deep learning for tasks such as data compression, image denoising, anomaly detection and facial recognition. Mar 31, 2022 · Unsupervised anomaly detection has been a point of interest to mitigate these limitations and develop reliable and secure networks. In this work, we Abstract—Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au-toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Previous works argued that training VAE models only with inliers is insufficient and the frame-work should be significantl In this work, we exploit the deep conditional variational autoencoder (CVAE) and we define an original loss function together with a metric that targets hierarchically structured data AD. The encoder in the AE outputs latent vectors. Unlike regular autoencoders that create fixed representations, VAEs create probability distributions. Expand [PDF] Semantic Reader Save to Library Create Alert Cite Dec 20, 2024 · We present an approach to constructing Conditional Variational Autoencoders (C-VAE) models with fuzzy inference during classification. hal-02396279 Published A Deep Set Variational Autoencoder is introduced and it is found that the method attains the best anomaly detection ability when there is no decoding step for the network, and the anomaly score is based solely on the representation within the encoded latent space. In Advances in Neural Information Processing Systems, pages 3483–3491, 2015. However, most existing methods face challenges of heterogeneity amongst feature subsets posed by non-independent and identically distributed (non-IID) data. This paper provides a comprehensive review of autoencoder architectures, from their inception and fundamental concepts to advanced implementations such as adversarial autoencoders However, achieving interpretable, likelihood-compatible compression and fast inference under weak model assumptions remains challenging. Autoencoders are self-supervised systems whose training goal is to compress (or encode) input data through dimensionality reduction and then accurately reconstruct (or decode) their Sep 18, 2023 · One of the lesser-explored but highly practical applications of generative AI is anomaly detection using Variational Autoencoders (VAEs). In this work, we Aug 18, 2025 · Variational autoencoders are commonly used in anomaly detection because of their reconstruction and generative capabilities. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful […] We conduct exten-sive experiments on the recently proposed MVTec anomaly detection dataset and present state-of-the-art anomaly local-ization results with just the standard VAE without any bells and whistles. By Time series Anomaly Detection (AD) plays a crucial role for web systems. Spherical latent spaces for stable variational autoencoders. Nov 28, 2022 · Anomaly detection is one of the most widespread use cases for unsupervised machine learning, especially in industrial applications. Abstract—Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au-toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Afterwards, outliers can be detected by their deviation from the probability model. 10805, 2018. Mar 12, 2025 · Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning models while data sources’ privacy is still preserved. Furthermore, in the scope of cyberattack detection, such Mar 11, 2024 · This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Previous works argued that training VAE models only with inliers is insufficient and the framework should be significantly modified in order to discriminate the anomalous instances. Compared to unconditional self-supervised reconstruction, our conditional architecture enhances both interpretability and generalizability. A common approach to anomaly detection is to identify outliers in a latent space learned from data. Dec 12, 2018 · Even though unsupervised approaches are usually less accurate, they are theoretically generalizable to all possible anomaly types and have the huge advantage of not needing any annotated training data. As Anomaly detection in time series with 973 robust variational quasi-recurrent autoencoders. Furthermore, ef ciency of VAE techniques, can be improved by data labelling adaptation, in their conditional version. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Additionally, the monitoring and detection scheme should be scalable to a wide variety of urban DPV systems, so that non-professional system owners could adopt them barrier-freely. Therefore, the equipment needs to be comprehensively monitored by an anomaly detection system to ensure its health. Among them, Variational AutoEncoder (VAE) is widely used, but it has the problem of over-generalization. 00270 . Jul 3, 2019 · Anomaly detection is a classical but worthwhile problem, and many deep learning-based anomaly detection algorithms have been proposed, which can usually achieve better detection results than traditional methods. 🚀 - sevdaimany/GenerativeAI-Models Convolutional Variational-Autoencoder (CVAE) for anomaly detection in time series. Mentioning: 52 - Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. have limited representation capabilities in the latent space and, hence, poor anomaly detection performance. What is Conditional Variational Autoencoder? 2Laboratoire de Recherche en Informatique (LRI) Université Paris-Saclay, Orsay, France Abstract—Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au- toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. The architectural configurations under consideration encompass the original VAE baseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE Oct 17, 2025 · Variational autoencoders (VAE) are machine learning models you can use to generate new data, process data from signals, detect anomalies, and more. Jun 11, 2022 · This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. In view of reconstruct ability of the model and the calculation of anomaly score, this paper proposes a time series anomaly detection method based on Variational AutoEncoder model(VAE Mar 12, 2021 · Anomaly detection constitutes a fundamental step in developing self-aware autonomous agents capable of continuously learning from new situations, as it enables to distinguish novel experiences from already encountered ones. Feb 27, 2024 · Additionally, we explore how VAEs have been applied in semi-supervised learning, anomaly detection, and data augmentation. These future directions offer exciting avenues for research, pushing the boundaries of detection accuracy, interpretability, and applicability across diverse domains. Apr 3, 2025 · Abstract A novel approach to detecting anomalies in time series data is presented in this paper. Our Abstract. Moreover, when conditional parameters are unavailable or model reliability is uncertain, our architecture can perform unsupervised power spectrum reconstruction and model anomaly detection. We propose a parameter-conditioned variational autoencoder (CVAE) that aligns a data-driven latent representation with cosmological parameters while remaining compatible with standard likelihood analyses. How-ever, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. CTVAE excels at generating high-quality synthetic samples that capture the intricate data distributions of both minority and majority classes. When performing anomaly detection, three types of anomalies are widely detected: global, contextual and collective anomalies [7]. Apr 10, 2025 · A conditional multimodal framework designed for multi-ROI anomaly detection in medical images has the potential to significantly enhance both the precision and flexibility of anomaly identification across various regions of interest. Jul 30, 2025 · By adding auxiliary information, like labels or other covariates, Conditional Variational Autoencoders (CVAEs) are strong deep generative models that enhance the performance of standard Variational Autoencoders (VAEs) and provide more precise control over the data creation process. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. Mar 15, 2025 · To overcome these limitations, we propose the Contrastive Tabular Variational Autoencoder (CTVAE), which integrates conditional Variational Autoencoders with contrastive learning techniques. Sep 1, 2024 · Unsupervised anomaly detection (UAD) is a diverse research area explored across various application domains. Existing approaches such as deep autoencoder (DAE), variational autoencoder (VAE), conditional variational autoencoder (CVAE) etc. Exploiting the rapid advances in probabilistic inference, in particular variational autoencoders (VAEs) for machine learning (ML) anomaly Nov 28, 2023 · In this work, we propose an anomaly detection framework based on conditional variational autoencoders and Chamfer loss to address the first issue and propose a novel architecture called CLIP-VAE that is tailored to future deployment to online data processing systems. In this repository, we provide a collection of peer reviewed literature about Unsupervised Anomaly Detection (UAD) in brain MRI. However Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. [2] In addition to being seen as an autoencoder neural network architecture, variational autoencoders can also be studied within the Jan 4, 2020 · Just like Fast R-CNN and Mask-R CNN evolved from Convolutional Neural Networks (CNN), Conditional Variational AutoEncoders (CVAE) and Variational AutoEncoders (VAE) evolved from the classic Explore the power of Conditional Variational Autoencoders (CVAEs) through this implementation trained on the MNIST dataset to generate handwritten digit images based on class labels. Distributed systems have been widely used in the information technology industry. The most common approaches for AD are based on One-Class Classification (OCC) [7]. There are already some deep learning models based on GAN for anomaly detection that demonstrate validity and accuracy on time series data sets. In Feb 29, 2020 · Recently, deep generative models have become increasingly popular in anomaly detection [10]. Over time, numerous anomaly detection techniques, including clustering, generative, and variational inference-based methods, are developed to address specific drawbacks and advance state-of-the-art techniques. Most of the existing research is based on the model of the generative model, which judges abnormalities by comparing the data errors between original samples and reconstruction samples. brhi eklk fua vnhwf qurpj xymv crnvl ibly kdb etnqnw qiwfm pzj vlpr oeae gydx