Research tackles novelty detection for mixed-type data, proposing probabilistic methods.
problem Detect anomalies in mixed-type datasets like numerical and categorical data.
method Experimental comparison of methods, probabilistic nonparametric model, autoencoder-based model.
result Developed robust methods for mixed-type data novelty detection.
Deep learning detects novel changes in time series data.
problem Detecting novel changes in time series with unknown probability structures.
method Causally extracts an innovations sequence for novelty detection.
result Minimax optimality established for the novelty detection method.
Proposes a novel method for detecting novelty in multi-modal data.
problem Challenges in detecting novelty in high-dimensional, multi-modal data.
method Orthogonalized latent space for disentangling features and defining novelty score.
result Proposed method outperforms state-of-the-art algorithms in novelty detection.
The paper models point pattern data and improves novelty detection.
problem Insufficient statistical models for point pattern data in classification and novelty detection.
method Proposes random finite sets (RFS) models with likelihood functions and maximum likelihood estimators for learning.
result Improves novelty detection performance with novel ranking functions based on RFS models.
Study compares methods for detecting novelty in textual data streams.
problem Lack of annotated datasets for novelty detection.
method Simulation framework to create controlled datasets and benchmark methods.
result Evaluation of state-of-the-art methods on different types of novelty.
Novel q-space abnormalities detected without labels.
problem Detecting unseen abnormalities in MRI scans.
method Training VAEs on normal data to identify abnormal samples in latent and output spaces.
result Many methods outperform existing q-space novelty detection techniques.
This study evaluates and compares novelty detection algorithms for discrete sequences.
problem Identifying anomalies in temporal data.
method Experimental comparison of state-of-the-art novelty detection methods on various public and industrial datasets.
result Recommendations for efficient and appropriate methods based on extensive experiments and scalability tests.
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
Flow-based models detect anomalies in industrial time series data.
problem Novelty detection in industrial time series data.
method Normalizing flows, specifically Masked Autoregressive Flows and Free-form Jacobian of Reversible Dynamics.
result Flow-based models outperform traditional methods in novelty detection of industrial time series data.
Paper proposes AdaDetect for FDR-controlled novelty detection.
problem Semi-supervised novelty detection with probabilistic classification.
method Data-adaptive learning of transformation to control FDR.
result Control of false discovery rate on detected novelties.
Proposes a context-aware approach to deep autoencoder novelty detection.
problem Challenges of static distribution in novelty detection.
method Semi-supervised network architecture with auxiliary labels for contextual information.
result Single model achieves performance of individually trained models on various contexts.
OCmst detects anomalies using CNN features and MSTs.
problem Novelty detection in data with no outliers.
method Uses CNN for feature extraction and MSTs for graph-based modeling.
result Achieved state-of-the-art results on CIFAR10 dataset.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
problem Understanding how learning-based novelty detection methods perform under adversarial perturbations.
method Formulated an oracle attack setup and evaluated vulnerability using black-box adversarial algorithms.
result Adversarial perturbations can significantly increase FDR while maintaining high detection power.
SVD-RND detects blurred images better than conventional methods.
problem Blurred images can fool conventional OOD detection schemes.
method Constructs a novel RND-based detector that uses blurred images during training.
result SVD-RND outperforms baseline detectors in various domains.
The paper develops methods for novelty detection on path space using signature-based statistics.
problem Novelty detection on path space as a hypothesis testing problem.
method Signature-based test statistics, transportation-cost inequalities, CVaR, one-class SVM algorithms.
result Established lower bounds on type-II error and general power bounds. AutoSciDACT detects scientific anomalies in noisy data.
problem Detecting anomalies in large, noisy scientific datasets.
method Contrastive pre-training for low-dimensional data representations, two-sample test using NPLM.
result Strong sensitivity to small anomalies across various scientific domains.
Proposes novel method for detecting novel scenarios in autonomous systems.
problem Detecting when a machine learning model makes a trustworthy prediction in dynamic, real-world situations.
method Leverages trained model's learned information and a new image similarity metric.
result Demonstrates the method's efficacy on real-world driving and indoor racing datasets.
New methods control false discoveries near the boundary in conformal novelty detection.
problem Over-optimistic assessments near the rejection threshold in conformal novelty detection.
method Support line (SL) correction and alternative procedures to control boundary false discovery rate (bFDR).
result New procedures control the boundary false discovery rate (bFDR) in the conformal setting.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
DCAE learns compact latent representations for one-class novelty detection.
problem Learning compact latent representations for one-class novelty detection.
method DCAE learns compact and collapse-free latent representations through internal discriminative layers of GANs, reconstructing in-class data finely and exclusively.
result DCAE achieves state-of-the-art performance on novelty and adversarial example detection.
The Familiarity Hypothesis explains deep open set methods' success in detecting novel objects.
problem Detecting novel objects in open set recognition problems.
method Logits-based detection of absence of familiar features.
result Familiarity-based detection fails in scenarios with both novel and familiar objects.
Paper tackles novelty detection in text classification.
problem Traditional text classification assumes known classes in testing, but often encounters unexpected instances.
method Converts problem to pair-wise matching, uses CNN with embedding matrices.
result Proposed method outperforms state-of-the-art baselines.
Robust VAE detects anomalies in corrupted data.
problem Detect anomalies in data with high corruption.
method Robust Variational Autoencoder (VAE) with four modifications.
result Establishes robustness to outliers and suitability to low-rank modeling.
New method detects unusual images in large datasets.
problem Detecting new images in large image data sets.
method Combines novelty detection with CNN image features.
result Rapid discovery with interpretable explanations.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
Generative Kernel PCA explores latent spaces for data interpretation and novelty detection.
problem Exploring latent spaces of datasets for better data interpretation.
method Generative Kernel PCA using hidden and visible units similar to Restricted Boltzmann Machines.
result Gradually moving in the latent space allows for interpretation of components and detection of novel patterns.
Proposes model-based approach for MI learning using point process theory.
problem Lack of statistical point pattern models in MI learning.
method Develops framework using point process theory for principled extensions of MI learning tasks.
result Tractable point pattern models and solutions for MI learning and decision making.
Simple regularization fixes easy detection of adversarial attacks.
problem Detecting adversarial machine learning attacks.
method Added a regularization term to the attacker's objective function.
result Regularization makes adversarial attacks detectable with simple methods.
Extends Mahalanobis distance to Banach spaces for anomaly detection.
problem Anomaly detection in infinite-dimensional spaces.
method Generalizes Mahalanobis distance to Banach spaces via Cameron-Martin norm and variance norm.
result Kernelized nearest-neighbour Mahalanobis distance outperforms traditional methods for time series novelty detection.
This work introduces a novel method to evaluate generative model novelty.
problem Evaluating the novelty of generative models compared to a reference model.
method Spectral approach to differential clustering and Kernel-based Entropic Novelty (KEN) score.
result The KEN score effectively detects novel modes and compares generative models.
New deep probabilistic model handles missing data in time series forecasting.
problem Handling missing data in time series forecasting.
method Combination of deep learning and probabilistic methods.
result Advantage in forecasting and novelty detection with missing data.
Framework detects cyber threats from Twitter tweets.
problem Time-consuming manual extraction of cyber threat intelligence.
method Novelty detection model trained on CVE data.
result F1-score of 0.643 for classifying cyber threat tweets.
SAGE improves memory efficiency by selectively adding, merging, or ignoring new facts.
problem Efficiently managing new facts in agentic LLMs to avoid costly write-time reasoning.
method SAGE uses a von Mises-Fisher-based density estimator to score and route candidate facts.
result SAGE achieves the best average token-F1 on LoCoMo and reduces add-phase API cost by 3.4x on GPT-4o-mini.
Paper proposes autonomous detection and learning from minimal data.
problem Autonomous detection and learning from extremely weak supervision.
method xClass method and algorithm for fully unsupervised detection and learning.
result Successfully discovers new classes and learns from data autonomously.
A novel outlier score detects new road infrastructure images.
problem Detecting newly observed road infrastructure images.
method Entropy-based outlier score using directed nearest neighbor graphs.
result High potential of the proposed technique in identifying outliers.
Generative deep models struggle with anomaly detection.
problem Comparing deep generative models to classical methods for anomaly detection.
method Statistical comparison of generative models on various datasets, varying hyperparameters.
result Deep generative models perform poorly when hyperparameters are selected with fewer anomalous samples.
A new method improves autoencoder-based out-of-distribution detection.
problem Detecting anomalies in images not seen during training.
method Incorporating Mahalanobis distance in latent space of autoencoders.
result Improved performance in detecting out-of-distribution samples.
KOD detects outliers in high-dimensional data.
problem Challenges in outlier detection in high-dimensional settings.
method Kernel transformation followed by projection pursuit approach with ensemble of directions and result combination.
result Empirical evaluations show effectiveness on various datasets.
A new method for student-initiated action advice using novelty detection.
problem Exploration and sample inefficiency in RL, especially with teacher absence.
method Random Network Distillation (RND) to measure advice novelty, updates only for advised states.
result Significant performance improvement over state-of-the-art methods, especially in challenging scenarios.
Paper tackles novel object recognition by improving hierarchical classification.
problem Challenges in recognizing novel object classes unseen during training.
method Proposes top-down and flatten methods for hierarchical novelty detection.
result Generates a hierarchical embedding leading to improved zero-shot learning performance.
A simple method for neural network confidence scores.
problem Measuring confidence in neural network predictions.
method Distance-based loss or Adversarial Training for data embedding.
result Significant improvement over traditional confidence scores.
S-MTGPR improves normative modeling of neuroimaging data.
problem Normative modeling of neuroimaging data without spatial covariance structure.
method Scalable multi-task Gaussian process regression (S-MTGPR) with low-rank approximation and Kronecker product.
result S-MTGPR provides higher sensitivity in novelty detection scenarios.
Deep learning model detects and classifies arrhythmia from ECG signals.
problem Detecting and classifying abnormal heartbeats (arrhythmia) from ECG signals.
method Use of topological data analysis in a modular neural network architecture for generalization.
result Model achieves state-of-the-art performance in arrhythmia detection and classification.
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics. The GLR statistics allow for an unknown post-change mean that represents an anomaly or novelty. We consider both fixed and time-varying projections, deri…
NN-EVCLUS uses neural networks to cluster data with uncertainty.
problem Clustering data with uncertainty and handling outliers.
method NN-EVCLUS learns a neural network to map attributes to mass functions, minimizing discrepancy between dissimilarities and conflict.
result NN-EVCLUS outperforms existing methods in clustering tasks.
Detecting edge correlation between two graphs sharpens a threshold based on densest subgraph.
problem Detecting edge correlation between two Erdős-Rényi graphs.
method Formulated as a hypothesis testing problem, connecting to densest subgraph detection.
result Sharp information-theoretic threshold established for edge correlation detection.
RAID algorithm detects anomalies in real-time IoT systems.
problem Anomaly detection limitations in multivariate dynamic processes.
method Adapts to non-stationary effects and handles data drift.
result Improved detection accuracy and root cause isolation.