May 2020
Intermediate to advanced
576 pages
26h 55m
French
Content preview from Deep Learning avec Keras et TensorFlow
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6.4 Architectures de CNN
243
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Bloc SE Bloc SE
Module Inception Unité résiduelle
Figure 6.20 – Module SE-Inception (à gauche) et unité SE-ResNet (à droite)
Un bloc SE analyse la sortie de l’unité à laquelle il est attaché, en se focalisant
exclusivement sur la profondeur (il ne recherche aucun motif spatial), et apprend les
caractéristiques qui sont généralement les plus actives ensemble. Il se sert ensuite de
cette information pour recalibrer les cartes de caractéristiques (voir la gure6.21).
Par exemple, un bloc SE peut apprendre que des bouches, des nez et des yeux se
trouvent généralement ...
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