Out-of-Distribution Detection Using Outlier Detection Methods

Diers, Jan; Pigorsch, Christian

doi:10.1007/978-3-031-06433-3_2

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…What is more, lots of statistic tests fail to estimate the real distribution of training data (data are not enough and the pdf are too coarse) [8]. Some methods, widely used across OoD detection on images are the Maximum Softmax Probability [12], the ODIN [13] and the Energybased OoD Detection [14], while others use outlier methods as the Isolation Forest [15]. Label shift in deep learning is also considered in [16].…”

Section: Related Workmentioning

confidence: 99%

Rule-based Out-Of-Distribution Detection

Bernardi¹,

Narteni²,

Cambiaso³

et al. 2023

Preprint

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Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity.

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Section: Related Workmentioning

confidence: 99%

Rule-based Out-Of-Distribution Detection

Bernardi¹,

Narteni²,

Cambiaso³

et al. 2023

Preprint

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“…According to [14], MATLAB created a method that focuses on an improved version of the Codebook algorithm for backdrop modeling and the straightforward edition of the Skeletonization algorithm for human tracking in a connected platform for real-time human motion detection. The pluses of this algorithm are that it detects very few false alarms compared to existing approaches while being more proficient than the existing research method.…”

Section: Introductionmentioning

confidence: 99%

“…The pluses of this algorithm are that it detects very few false alarms compared to existing approaches while being more proficient than the existing research method. They specify that the equipment used in this method should be improved, and that vastly larger spaces are required for this algorithm to be faster and potentially more suitable for deployment [14].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Social Distancing Tracking and Monitoring System (SDMOS-19)

Binti Abdullah,

Kamarudin,

Makhtar

et al. 2024

JOIV : Int. J. Inform. Visualization

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The Coronavirus disease (COVID-19), stemming from the SARS-CoV-2 virus, has garnered global concern as a virulent infectious ailment. Recognized as an epidemic by the World Health Organization (WHO), the persistently mutating virus sustains its transmission within communities. Individuals have been advised to uphold a safe interpersonal distance, notably around five feet, to mitigate its spread during social interactions. Addressing this imperative, an innovative automated social distancing detection system is conceived, leveraging the Convolutional Neural Network (CNN) algorithm. This system operates on two distinct input modes: static images and recorded videos recorded on closed-circuit television (CCTV). Remarkably, the proposed automated system adeptly quantifies and surveils the extent of social distancing among individuals in densely populated settings. A sophisticated framework accurately discerns social distancing compliance, delineating between hazardous and secure intervals via distinct red and green bounding box indicators. The culmination of this endeavor reveals an impressive 90% detection accuracy for both input modes. Notably, this proposed system holds substantial promise for implementation within sprawling premises such as expansive shopping malls or recreational parks. Seamlessly enforcing automated safety distance assessment expedites real-time insights to security departments and other relevant authorities. Consequently, the efficacy of citizens in upholding safe interpersonal distances can be promptly evaluated and, if necessary, corrective measures can be expeditiously instituted. This automated system ensures public health and safety maintenance, particularly in difficult circumstances.

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