Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: 10.1109/TIM.2021.3098381

Event: Veröffentlichung

(where): Aachen

(who): Universitätsbibliothek der RWTH Aachen

(when): 2021

Creator: Rippel, Oliver Christoph Johannes
Mertens, Patrick
Konig, Eike
Merhof, Dorit

DOI: 10.18154/RWTH-2021-09616

URN: urn:nbn:de:101:1-2021102203284183689289

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:44 PM CET

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Rippel, Oliver Christoph Johannes
Mertens, Patrick
Konig, Eike
Merhof, Dorit
Universitätsbibliothek der RWTH Aachen

Time of origin

2021

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Local feature description with invariance against affine projection

Spectral super-resolution: deep learning and the physical perimeter of metamerism

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Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

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Other Objects (12)

Reconstruction and visualization of neuronal pathways from diffusion tensor data : = Rekonstruktion und Visualisierung neuronaler Bahnen aus Diffusionstensor-Daten

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Brightness Invariant Deep Spectral Super-Resolution

Reconstruction and visualization of neuronal pathways from diffusion tensor data

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Local feature description with invariance against affine projection

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