scholarly journals Perfect Density Models Cannot Guarantee Anomaly Detection

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1690
Author(s):  
Charline Le Lan ◽  
Laurent Dinh
Keyword(s):  
Active Learning ◽  
Anomaly Detection ◽  
Curse Of Dimensionality ◽  
Generative Models ◽  
Uncertainty Estimation ◽  
Meaningful Information

Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this paper, we take a closer look at the behavior of distribution densities through the lens of reparametrization and show that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality. We conclude that the use of these likelihoods for anomaly detection relies on strong and implicit hypotheses, and highlight the necessity of explicitly formulating these assumptions for reliable anomaly detection.

scholarly journals Predictive Uncertainty Estimation for Tractable Deep Probabilistic Models

2020 ◽  
Author(s):  
Julissa Villanueva Llerena
Keyword(s):  
Probabilistic Models ◽  
Linear Time ◽  
Generative Models ◽  
Uncertainty Estimation ◽  
Image Completion ◽  
Predictive Uncertainty ◽  
Learning Tasks ◽  
Challenging Tasks ◽  
Statistical Support ◽  
Marginal Inference

Tractable Deep Probabilistic Models (TPMs) are generative models based on arithmetic circuits that allow for exact marginal inference in linear time. These models have obtained promising results in several machine learning tasks. Like many other models, TPMs can produce over-confident incorrect inferences, especially on regions with small statistical support. In this work, we will develop efficient estimators of the predictive uncertainty that are robust to data scarcity and outliers. We investigate two approaches. The first approach measures the variability of the output to perturbations of the model weights. The second approach captures the variability of the prediction to changes in the model architecture. We will evaluate the approaches on challenging tasks such as image completion and multilabel classification.

Anomaly detection driven active learning for identifying suspicious tracks and events in WAMI video

2012 ◽  
Author(s):  
David J. Miller ◽  
Aditya Natraj ◽  
Ryler Hockenbury ◽  
Katherine Dunn ◽  
Michael Sheffler ◽  
...  
Keyword(s):  
Active Learning ◽  
Anomaly Detection

scholarly journals SALAD: An Exploration of Split Active Learning based Unsupervised Network Data Stream Anomaly Detection using Autoencoders

2021 ◽  
Author(s):  
Christopher Nixon ◽  
Mohamed Sedky ◽  
Mohamed Hassan
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Data Streams ◽  
Data Stream ◽  
Learning Strategy ◽  
Network Data ◽  
Anomaly Detector ◽  
Active Learning Strategy

<div>Machine learning based intrusion detection systems monitor network data streams for cyber attacks. Challenges in this space include detection of unknown attacks, adaptation to changes in the data stream such as changes in underlying behaviour, the human cost of labeling data to retrain the machine learning model and the processing and memory constraints of a real-time data stream. Failure to manage the aforementioned factors could result in missed attacks, degraded detection performance, unnecessary expense or delayed detection times. This research evaluated autoencoders, a type of feed-forward neural network, as online anomaly detectors for network data streams. The autoencoder method was combined with an active learning strategy to further reduce labeling cost and speed up training and adaptation times, resulting in a proposed Split Active Learning Anomaly Detector (SALAD) method. The proposed method was evaluated with the NSL-KDD, KDD Cup 1999, and UNSW-NB15 data sets, using the scikit-multiflow framework. Results demonstrated that a novel Adaptive Anomaly Threshold method, combined with a split active learning strategy offered superior anomaly detection performance with a labeling budget of just 20%, significantly reducing the required human expertise to annotate the network data. Processing times of the autoencoder anomaly detector method were demonstrated to be significantly lower than traditional online learning methods, allowing for greatly improved responsiveness to attacks occurring in real time. Future research areas are applying unsupervised threshold methods, multi-label classification, sample annotation, and hybrid intrusion detection.</div>

scholarly journals The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

2021 ◽  
Author(s):  
Paul Bergmann ◽  
Kilian Batzner ◽  
Michael Fauser ◽  
David Sattlegger ◽  
Carsten Steger
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Structural Changes ◽  
Performance Metrics ◽  
Generative Models ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Advantages And Disadvantages ◽  
Unsupervised Anomaly Detection

AbstractThe detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec anomaly detection dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth annotations for all anomalies. We conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pretrained convolutional neural networks, as well as classical computer vision methods. We highlight the advantages and disadvantages of multiple performance metrics as well as threshold estimation techniques. This benchmark indicates that methods that leverage descriptors of pretrained networks outperform all other approaches and deep-learning-based generative models show considerable room for improvement.

scholarly journals Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties

2021 ◽  
Author(s):  
Nicolae C. Iovanac ◽  
Robert MacKnight ◽  
Brett Savoie
Keyword(s):  
Active Learning ◽  
Quantum Chemistry ◽  
Chemical Space ◽  
Cost Effective ◽  
Generative Models ◽  
Learning Approach ◽  
The Novel ◽  
Practical Applications ◽  
Chemical Models ◽  
And Shifts

<p>Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular searches in chemical space. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry to obtain the true values, and these new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models’ understanding of unknown areas of chemical space and shifts the distribution of generated compounds towards the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the B97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously.</p>

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