A new method combines generative and feature-based approaches for unsupervised anomaly detection.
problem Identifying subtle anomalies in test samples compared to a normative distribution.
method A generative cold-diffusion pipeline trained to restore synthetically-corrupted images, combined with a novel synthetic anomaly generation procedure and ensembling restorations.
result Surpasses prior state-of-the-art for unsupervised anomaly detection in three Brain MRI datasets.
CANARI detects near-anomalies to predict future anomalies proactively.
problem Uncertainty in anomaly detection near distribution boundaries.
method Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI) method.
result CANARI outperforms baseline methods in detecting near-anomalies and predicting future anomalies.
Fence GAN improves anomaly detection by modifying GAN loss.
problem Anomaly detection on complex high-dimensional data.
method Modified GAN loss to encourage generated samples at the boundary of real data distribution.
result Fence GAN yields the best anomaly classification accuracy.
Generative adversarial networks generate synthetic anomalies for host-based intrusion detection.
problem Imbalanced datasets in host-based intrusion detection.
method Cycle-GAN to generate synthetic anomalies from normal data.
result Improved anomaly detection with higher AUC and anomaly detection rate.
DRAMA detects anomalies in high dimensions via dimensionality reduction.
problem Challenges in anomaly detection for large datasets in high dimensions.
method Dimensionality reduction and unsupervised clustering.
result DRAMA is robust and competitive in high dimensions, especially for online anomaly detection.
Swift Hydra uses RL and generative AI to improve anomaly detection.
problem Generalization to unseen anomalies in critical systems.
method Generative AI and reinforcement learning (RL) for synthesizing diverse anomaly samples.
result Swift Hydra outperforms state-of-the-art models on ADBench benchmark.
Wasserstein GAN improves anomaly detection on time series data.
problem Anomaly detection in time series datasets.
method Wasserstein GAN for learning normal data distribution and anomaly detection using a stacked encoder.
result W-GAN with encoder achieves state-of-the-art anomaly detection scores on MNIST and multi-variate time series.
TimeVQVAE-AD detects anomalies in time series data with high accuracy and provides explainable results.
problem Detecting and explaining anomalies in time series data accurately.
method Masked latent generative modeling in time-frequency domain.
result TimeVQVAE-AD outperforms existing methods in anomaly detection and explainability.
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution methods.
Paper proposes a method to generate synthetic anomalies for robust anomaly detection.
problem Anomaly detection struggles with unbalanced data and rare anomalies.
method Two-level hierarchical latent space representation for feature distillation and synthesis.
result The method creates robust synthetic anomalies for training robust binary classifiers.
Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection…
New anomaly formulas from E8 bundles.
problem Anomaly cancellation in characteristic forms.
method Constructing modular forms using E8 bundles.
result New anomaly cancellation formulas.
MEMGAN uses memory to improve anomaly detection by isolating abnormal data.
problem Weak guarantees for detecting anomalous data in classical algorithms.
method Memory-augmented Generative Adversarial Networks (MEMGAN) with a memory module.
result MEMGAN provides strong guarantees for anomaly detection with improved reconstruction.
PReNet detects seen and unseen anomalies using pairwise relations.
problem Detecting unseen anomalies in semi-supervised learning.
method Pairwise Relation prediction Network (PReNet) learns anomaly and normal patterns.
result PReNet significantly outperforms nine competing methods in anomaly detection.
Research shows deep generative models' likelihoods are unreliable for anomaly detection.
problem Anomaly detection using deep generative models' likelihoods is unreliable.
method Examined the behavior of distribution densities through reparametrization.
result The likelihoods used for anomaly detection rely on strong and implicit hypotheses.
Detects anomalies without training data using deep learning.
problem Detect anomalies in data without labeled training data.
method Inverse Generative Adversarial Network (GAN) for semi-supervised learning.
result Successfully detects anomalies in data without labeled training data.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
TAnoGan detects anomalies in time series data using GANs.
problem Anomaly detection in time series data.
method Generative Adversarial Networks (GAN) for unsupervised anomaly detection.
result TAnoGan outperforms traditional and neural network models in anomaly detection.
Method explains anomaly detection by generating normal modifications.
problem Complexity of deep learning methods in anomaly detection.
method Generates multiple alternative modifications for anomalies.
result High-quality semantic explanations provided for anomaly detection.
SAFAVI framework detects anomalies without a single best solution.
problem No single anomaly detector works best across all domains.
method Abstracts and unifies anomaly detection tasks, allowing flexible comparisons.
result No single detector is superior for all cases in streaming data.
By studying modular invariance properties of some characteristic forms, we prove some new anomaly cancellation formulas which generalize the Han-Zhang and Han-Liu-Zhang anomaly cancellation formulas
Study examines how discretization affects anomaly detection in datasets.
problem Detecting six types of anomalies in datasets using different discretization methods.
method Conducted experiments with SECODA, a general-purpose algorithm for unsupervised anomaly detection.
result Different discretization methods favor the discovery of certain anomaly types.
TailGAN uses GANs to detect anomalies near data distribution tails.
problem Anomaly detection near data distribution tails with current GAN limitations.
method TailGAN leverages GANs with maximum entropy regularization to generate and detect anomalies near data distribution tails.
result TailGAN achieves competitive performance on various datasets compared to existing methods.
Develops an anomaly detection system using synthetic data.
problem Automatic anomaly detection for meteorological time-series.
method Constructs an ensemble of anomaly detectors using synthetic data and adaptive threshold selection.
result Demonstrates the efficiency of the method in a real-world application.
Anomaly Awareness detects anomalies in particle physics and computer vision.
problem Detect anomalies in complex data sets.
method Modifies cost function to learn normal events and anomalies.
result Effective at identifying new anomalies not previously seen.
In this paper, we present Paranom, a parallel anomaly dataset generator. We discuss its design and provide brief experimental results demonstrating its usefulness in improving the classification correctness of LSTM-AD, a state-of-the-art anomaly detection model.
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
problem Detecting axle-box bearing anomalies with only normal training data.
method Two-stage approach: pseudo anomalies, contrastive learning, KNN.
result Improves anomaly detection separability and sensitivity to degradation.
MDGAN generates additional samples for better anomaly detection.
problem Challenges in obtaining anomalous samples for training.
method Multi-Discriminator GAN architecture with two discriminators.
result MDGAN improves anomaly detection performance.
TadGAN detects anomalies in time series data using GANs and LSTM.
problem Challenges in detecting anomalies in time series data, especially without labeled data.
method TadGAN uses Generative Adversarial Networks (GANs) with LSTM Recurrent Neural Networks to capture temporal correlations and compute anomaly scores.
result TadGAN outperforms 8 baseline methods in most cases, achieving the highest averaged F1 score.
A new framework detects anomalies in structured data.
problem Detecting anomalies in samples not conforming to low-dimensional manifolds.
method Preference Isolation Forest (PIF) framework combining adaptive isolation methods and preference embedding.
result Anomalies identified as isolated points in a high-dimensional preference space.
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
Anomaly flow studied on flat and non-flat nilmanifolds.
problem Analyzing the Anomaly flow on nilmanifolds.
method Examined with respect to Hermitian connections, focusing on flat and non-flat cases.
result General solutions and qualitative behavior of the Anomaly flow on nilmanifolds.
Proposes combining SARIMA and STL for real-time anomaly detection.
problem Need for accurate and fast anomaly detection systems for massive data.
method Combines SARIMA and STL models for anomaly detection.
result Demonstrates high accuracy in detecting anomalies in noisy, non-periodic data.
A new model combines VAE and GAN for better anomaly detection in imbalanced datasets.
problem Anomaly detection in imbalanced datasets, especially in medical applications.
method β-VAEGAN model combining VAE and GAN, kernelized SVM for anomaly scores, and deviation from Gaussian prior.
result Improved F1 score from 0.85 to 0.92 on MITBIH Arrhythmia Database. Paper revisits PCA for anomaly detection in network security.
problem Understanding and improving anomaly detection in network security.
method Revisit probabilistic PCA model and its connection to MSNM framework.
result Mathematical model connects PCA to MSNM for anomaly detection.
Improved anomaly detection for incipient faults using ensemble learning.
problem Difficulty in detecting milder anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning improves performance on incipient anomaly detection.
We classify higher-SPTs and their anomalies via cobordism theory.
problem Understanding higher symmetries and anomalies in quantum field theories.
method Developed a generalized cobordism theory using advanced mathematical tools.
result Classified higher-SPTs and their boundary anomalies.
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine …
Improved anomaly detection using adversarial mirrored autoencoders.
problem Detecting out-of-distribution samples in machine learning.
method Adversarial Mirrored Autoencoder (AMA) with latent space regularization.
result AMA improves anomaly detection performance on OOD detection benchmarks.
For even dimensional manifolds, we prove some twisted anomaly cancellation formulas which generalize some well-known cancellation formulas. For odd dimensional manifolds, we obtain some modularly invariant characteristic forms by the Chern-Simons transgression and we also get some twisted anomaly cancellation formulas.
Paper solves optimal transport with neural nets, also detects anomalies.
problem Optimal transport problems with twist conditions.
method Primal-dual algorithm for neural networks.
result Solves financial data generation and anomaly detection.
ACE explains security anomaly detection models through feature contributions.
problem Understanding which features contribute to security anomalies.
method Regression framework to locally approximate anomaly scores.
result Identifies correct contributing features in synthetic data and real data.
Proposes a self-adversarial variational autoencoder for anomaly detection.
problem Anomaly detection in deep generative models.
method Self-adversarial Variational Autoencoder with Gaussian anomaly prior.
result Significant improvements in anomaly detection performance compared to SOTA baselines.
OMASGAN generates anomalous samples on distribution boundary to improve anomaly detection.
problem Missed anomalies and low AD performance due to OoD samples in generative models.
method OMASGAN generates anomalous samples on the estimated distribution boundary using a GAN approach, refining AD models.
result OMASGAN improves AD performance by at least 0.24 and 0.07 points on average on MNIST and CIFAR-10 datasets.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
GANs improve anomaly detection accuracy.
problem Detecting unseen anomalies is challenging.
method Adversarial training of GANs.
result Remarkable results on anomaly detection.
A statistical test controls false positives in anomaly localization using diffusion models.
problem Uncertainty and bias in generative models for anomaly localization.
method Selective inference to quantify significance and control false positives.
result The method effectively controls false positive detection rates.
VELC model detects anomalies in time series data better than existing methods.
problem Anomaly detection in time series data.
method VELC model based on Variational AutoEncoder with re-Encoder and Latent Constraint network.
result VELC model outperforms state-of-the-art methods on benchmark datasets.