Results 11 to 20 of about 161,068 (368)
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context ...
Faruk Ahmed, Aaron C. Courville
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Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder
Energy Reports, 2022 Marine diesel engine with high thermal efficiency and good economy has become the main power of ships. Anomaly detection is an important method to improve the operation reliability of marine diesel engine.
Chong Qu +3 more
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Joaggi/Incremental-Anomaly-Detection-using-Quantum-Measurements: v1.0.0 [PDF]
, 2022 Version 1 Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, such as conceptual drift ...
Joagg
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Improvement in detection of presence in forbidden locations in video anomaly using optical flow map [PDF]
هوش محاسباتی در مهندسی برق, 2023 Anomaly detection has been in researchers’ scope of study for a long time. The wide variety of anomaly detection use cases ranges from quality control in production lines to providing security in public places.
Mohammad Rahimpour +3 more
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Anomaly detection in video sequences: A benchmark and computational model
IET Image Processing, 2021 Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale.
Boyang Wan +4 more
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Autonomous anomaly detection [PDF]
2017 Evolving and Adaptive Intelligent Systems (EAIS), 2017 In this paper, a new approach for autonomous anomaly detection is introduced within the Empirical Data Analytics (EDA) framework. This approach is fully data-driven and free from thresholds. Employing the nonparametric EDA estimators, the proposed approach can autonomously detect anomalies in an objective way based on the mutual distribution and ...
Gu, Xiaowei, Angelov, Plamen Parvanov
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Detecting Floor Anomalies [PDF]
Procedings of the British Machine Vision Conference 1994, 1994 When a robot moves about a 2D world such as a planar surface, it is important that obstacles to the robot's motions be detected. This classical problem of "obstacle detection" has proven to be difficult. Many researchers have formulated this problem as being the process of determining where a robot cannot move due to the presence of obstacles.
Michael R. M. Jenkin, Allan D. Jepson
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Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature
IEEE Access, 2019 Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection ...
Yuanyi Chen +3 more
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Suspicious and Anomaly Detection
CoRR, 2022 7 pages, 10 ...
Shubham Deshmukh +4 more
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Comparing anomaly detection methods in computer networks [PDF]
, 2010 This work in progress outlines a comparison of anomaly detection methods that we are undertaking. We are comparing different types of anomaly detection methods with the purpose of achieving results covering a broad spectrum of anomalies.
Löf, Andreas +3 more
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