A novel unsupervised outlier detection method using Randomized PCA Forest.
problem Unsupervised outlier detection in datasets.
method Randomized Principal Component Analysis (RPCA) Forest for deriving an outlier score.
result Superior performance compared to classical and state-of-the-art methods.
Study proposes using auxiliary classification to improve unsupervised anomaly detection.
problem Challenging anomaly detection in high-dimensional data.
method Use of an auxiliary classification task to extract features from unlabelled data by supervised learning.
result Our feature learning approach yields best anomaly detection performance.
Ensemble unsupervised anomaly detection using IRT for hidden ground truth.
problem Challenges in constructing an ensemble from unsupervised anomaly detection methods.
method Use Item Response Theory to compute latent traits and construct an ensemble that downplays noisy methods.
result Demonstrated effectiveness of IRT ensemble on extensive data repository.
Improved unsupervised anomaly detection using Random Forest.
problem Enhancing unsupervised anomaly detection accuracy.
method Training Random Forest to distinguish real and synthetic data, then applying transformed distances.
result Significant improvement in anomaly detection accuracy compared to other methods.
Research explores unsupervised methods for detecting vessel behavior changes in real-time data streams.
problem Detecting shifts in vessel behavior for maritime traffic monitoring.
method Investigates unsupervised and semi-supervised change detection methods.
result Identifies shifts in vessel behavior for unusual events detection.
XGBOD combines unsupervised and supervised methods for better outlier detection.
problem Enhanced outlier detection from normal observations in various datasets.
method Hybrid approach using unsupervised representation learning to improve a supervised classifier.
result XGBOD outperforms competing methods across seven datasets.
Unsupervised model detects healthcare fraud from patient visit data.
problem Detecting fraudulent healthcare bills from patient visit data.
method Uses LSTM and seq2seq models for anomaly detection, normalizes scores with EDF.
result Improves anomaly detection for high class imbalance problems.
An active learning method enhances deep learning for unsupervised anomaly detection.
problem Mathematical definition of anomalies is hard; performance guarantees in unsupervised anomaly detection require priors.
method Introduces a new layer that transforms deep learning models into active learning methods.
result Effective separation of outliers from normal data on synthetic and real datasets.
SPI detects anomalies using privileged information from training data.
problem Unsupervised anomaly detection in the absence of labeled test data.
method Constructs density estimates in privileged space and transfers them to anomaly scoring space.
result Significant improvement in anomaly detection performance with privileged information.
This paper uses β-VAE for unsupervised anomaly detection in NSL-KDD.
problem Unsupervised anomaly detection in network traffic.
method β-VAE with latent space structure and reconstruction error.
result Latent space exploitation is more effective for classification tasks.
Modified neural network enhances unsupervised anomaly detection.
problem Unsupervised anomaly detection in multimodal data.
method Neural network with modified random projection outlyingness.
result Performance comparable to state-of-the-art methods.
ABC uses autoencoders for better anomaly detection.
problem Detecting both known and unknown anomalies accurately.
method Probabilistic binary classifier using Autoencoder for normal data reconstruction.
result ABC outperforms existing methods in anomaly detection.
A new framework uses information theory to detect anomalies in images without labeled data.
problem Detect anomalies in images without labeled data.
method A direct objective function using information theory to maximize the distance between normal and anomalous data.
result The proposed framework significantly outperforms state-of-the-arts on multiple benchmark datasets.
Enhances weather detection by learning from auxiliary information.
problem Mispredictions in unsupervised severe weather detection.
method Learning joint representations of textual and weather data.
result Improved decision boundaries for severe weather detection.
A new method detects outliers using ensembles of Dirichlet process mixtures.
problem Challenges in unsupervised outlier detection using Dirichlet process mixtures.
method Ensembles of Dirichlet process Gaussian mixtures with random subspace and subsampling.
result Empirically outperforms existing approaches in unsupervised outlier detection.
Deep semi-supervised anomaly detection improves fraud detection in financial markets.
problem Detecting fraud in high-frequency financial data with limited labeled examples.
method Evaluation of Deep Semi-Supervised Anomaly Detection (Deep SAD) on proprietary limit order book data.
result Deep SAD significantly improves fraud detection accuracy with minimal labeled data.
Proposes manifold-based unsupervised anomaly detection for visual data.
problem Rare anomalies in unlabeled data.
method Constant curvature manifolds, hyperspherical Variational Auto-Encoders (VAE) with gyroplane layer.
result State-of-the-art results on visual anomaly benchmarks and histopathology.
New method improves fault detection by adding unsupervised learning to Monte Carlo dropout models.
problem Detecting and diagnosing incipient and unknown faults in deep neural networks.
method Augmenting Monte Carlo dropout models with unsupervised learning tasks.
result Improved fault detection and diagnosis performance, especially on out-of-distribution examples.
Proposes a method for anomaly detection with inexact labels.
problem Handling anomaly detection with inexact labels.
method Trains an anomaly score function using a neural network-based unsupervised method, maximizing the inexact AUC.
result Improves anomaly detection performance with inexact labels and outperforms existing methods.
A new method detects concept drift without true labels.
problem Detecting concept drift in unsupervised settings.
method Student-teacher learning paradigm for drift detection.
result The method outperforms state-of-the-art approaches in experiments.
New method detects change points in quasi-periodic signals without supervision.
problem Detecting change points in complex, non-harmonic signals.
method Optimal transport theory, topological analysis, bootstrap procedure.
result Successfully detects abnormal cardiac cycles in various arrhythmias.
ARGUE combines expert networks for anomaly detection.
problem Anomaly detection without labeled data.
method Gated mixture-of-experts architecture combining expert networks.
result Prior knowledge about normal data distribution is valuable.
Paper proposes a new unsupervised clustering method using attention models.
problem Unsupervised community detection on graphs.
method Optimizes soft modularity loss on Bethe Hessian embeddings.
result Model performs competitively with classical and GNN methods.
SUOD accelerates unsupervised outlier detection for large datasets.
problem Scalability issues in unsupervised outlier detection for high-dimensional datasets.
method Three-module acceleration framework: Random Projection, Balanced Parallel Scheduling, Pseudo-supervised Approximation.
result SUOD significantly improves efficiency and scalability in outlier detection.
Proposes methods to improve interpretability of Isolation Forest for anomaly detection.
problem Lack of interpretability in Isolation Forest.
method Defines feature importance scores and unsupervised feature selection methods.
result Improves interpretability of Isolation Forest for anomaly detection.
Detects anomalies in autonomous mobile robots using vision.
problem Anomaly detection for autonomous mobile robots.
method Unsupervised deep learning methods and a novel dataset.
result State-of-the-art approach tested on a new dataset.
Unsupervised deep learning detects and localizes crop leaf diseases.
problem Automated detection and localization of crop diseases.
method Three types of autoencoders (CAE, CVAE, VQ-VAE) applied to an open-source dataset.
result VQ-VAE autoencoder outperforms in image reconstruction, anomaly removal, detection, and localization.
BAE uses boosting to improve autoencoder ensembles for robust outlier detection.
problem Overfitting in autoencoders limits their effectiveness in unsupervised outlier detection.
method Boosting-based Autoencoder Ensemble (BAE) trains autoencoders sequentially with weighted sampling to reduce outliers and inject diversity.
result BAE outperforms state-of-the-art approaches in various outlier detection conditions.
Generates synthetic data for benchmarking unsupervised outlier detection.
problem Difficulty in benchmarking unsupervised outlier detection due to rare and varied outliers in real data.
method Proposes a generic process to generate synthetic data with insightful characteristics.
result Demonstrates practicality of the generic process through a benchmark with state-of-the-art detection methods.
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.
New unsupervised image translation method detects changes without labeled data.
problem Detecting changes in images without labeled data.
method Affinity-based change priors and weighted loss functions trained on convolutional neural networks.
result Proposed method outperforms state-of-the-art algorithms in detecting changes.
Paper proposes a new dataset for group anomaly detection in physics.
problem Group anomaly detection in physics.
method Development of a synthetic benchmark dataset and comparison of anomaly detection techniques.
result Demonstrated performance of anomaly detection techniques on the new dataset.
Anomaly detection method separates contextual from behavioral attributes.
problem Detect anomalies in data without labeled examples.
method Uses joint deep variational generative models.
result Robust to anomalous or novel contextual attributes.
Donut detects anomalies in web KPIs without labels.
problem Anomaly detection for seasonal KPIs with varying patterns and data quality.
method Unsupervised anomaly detection via Variational Auto-Encoder (VAE) with key techniques.
result Donut outperforms state-of-the-art approaches, achieving F-scores up to 0.9.
New method detects anomalies without labeled data.
problem Detecting anomalies in unsupervised settings.
method Nonlinear manifold learning using LMGP or AE.
result Superior performance compared to existing methods.
ceVAE improves anomaly detection in medical images by combining reconstruction and density-based scoring.
problem Lack of formal assertions and comparability in anomaly scores based on reconstruction error.
method Proposes Context-encoding Variational Autoencoder (ceVAE) that combines reconstruction and density-based scoring.
result ceVAE achieves unsupervised ROC-AUCs of 0.95 and 0.89 on BraTS-2017 and ISLES-2015 benchmarks, outperforming state-of-the-art methods.
Study examines unsupervised and graph-based methods for anomaly detection in IoBT, outperformed by supervised stacking ensemble.
problem Anomaly detection in adversarial environments of IoBT.
method Unsupervised learning, graph-based methods, ensemble supervised learning, adversarial training.
result Supervised stacking ensemble method outperforms unsupervised and graph-based methods in detecting anomalies.
Self-supervised model detects phoneme boundaries without annotations.
problem Unsupervised phoneme segmentation without manual annotations.
method Convolutional neural network trained with Noise-Contrastive Estimation.
result Model outperforms baselines on TIMIT and Buckeye corpora.
Generative data augmentation improves unsupervised anomaly detection.
problem Improving anomaly detection performance in unsupervised settings.
method Oversampling infrequent normal samples using adversarial autoencoder (AAE) to transform high-dimensional multimodal data into low-dimensional unimodal latent distributions.
result Consistent improvements in anomaly detection across various real-world datasets.
Unified benchmark for GLAD and GLOD methods across 35 datasets.
problem Gap between GLAD and GLOD research due to distinct evaluation setups.
method Comprehensive evaluation framework that unifies GLAD and GLOD.
result Multi-dimensional analyses of existing methods' strengths and limitations.
Unsupervised image regression detects changes in satellite images.
problem Detecting changes in heterogeneous multitemporal satellite images.
method Comparison of affinity matrices and image regression.
result Image regression improves change detection accuracy.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to d…
Proposes a novel Out-of-Bag anomaly detection method for ML systems.
problem Challenges of detecting data anomalies in real-world datasets.
method Model-based approach decomposing unsupervised problem into ensemble models using Out-of-Bag estimates.
result Demonstrates state-of-the-art performance and improved accuracy in ML systems.
Deep metric learning detects anomalies without labels.
problem Unsupervised anomaly detection for high-dimensional data.
method Deep metric learning with end-to-end optimization, data distillation, hard mining.
result Significant performance gains over state-of-the-art methods.
SUOD accelerates OD for large, diverse models.
problem Training and scoring new samples with many unsupervised, heterogeneous OD models.
method Data reduction, model approximation, and taskload optimization.
result SUOD accelerates OD for over 20 benchmark datasets and a real-world case.
An encoder-decoder model detects anomalies in manufacturing data.
problem Detecting and predicting anomalies in sequential sensor data.
method Encoder-decoder architecture for unsupervised anomaly detection.
result The model identifies anomalies and predicts future states in manufacturing processes.
Paper proposes a method to detect unknown anomalous sounds without training data using deep learning and Neyman-Pearson lemma.
problem Unsupervised detection of unknown anomalous sounds in audio data.
method Uses an autoencoder to minimize reconstruction error of normal sounds and Neyman-Pearson lemma to maximize true positive rate under low false positive rate conditions.
result The proposed method improves performance measures of unsupervised anomaly detection in audio data under low false positive rate conditions.
We present a sparse and invariant representation with low asymptotic complexity for robust unsupervised transient and onset zone detection in noisy environments. This unsupervised approach is based on wavelet transforms and leverages the scattering network from Mallat et al. by deriving frequency invariance. This frequ…