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pro vyhledávání: '"Ndiour, Ibrahima J"'
This paper presents a fast, principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN). We propose the application of linear statistical dimensionality reduction techniques on the semantic features
Externí odkaz:
http://arxiv.org/abs/2203.10422
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem. In this setup, we have access to only anomaly-free training data and want to detect and identify a
Externí odkaz:
http://arxiv.org/abs/2105.03270
Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we investigate backdoor data poisoning attack on deep neural networks (DNNs)
Externí odkaz:
http://arxiv.org/abs/1912.01206
Akademický článek
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Autor:
Ndiour, Ibrahima Jacques
This thesis tackles the visual tracking problem as a target contour estimation problem in the face of corrupted measurements. The major aim is to design robust recursive curve filters for accurate contour-based tracking. The state-space representatio
Externí odkaz:
http://hdl.handle.net/1853/37283