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The projected belief network classifier: Both generative and discriminative

 
: Baggenstoss, P.M.

:

Heusdens, Richard (Hrsg.) ; European Association for Signal Processing -EURASIP-:
28th European Signal Processing Conference, EUSIPCO 2020. Proceedings : 24-28 August 2020, Amsterdam, the Netherlands
Amsterdam: EURASIP, 2020
ISBN: 978-9-0827-9705-3
ISBN: 978-9-08279-704-6
ISBN: 978-1-7281-5001-7
S.795-799
European Signal Processing Conference (EUSIPCO) <28, 2020, Amsterdam/cancelled>
European Signal Processing Conference (EUSIPCO) <28, 2021, Online>
Englisch
Konferenzbeitrag
Fraunhofer FKIE ()

Abstract
The projected belief network (PBN) is a layered generative network with tractable likelihood function, and is based on a feed-forward neural network (FF-NN). It can therefore share an embodiment with a discriminative classifier and can inherit the best qualities of both types of network. In this paper, a convolutional PBN is constructed that is both fully discriminative and fully generative and is tested on spectrograms of spoken commands. It is shown that the network displays excellent qualities from either the discriminative or generative viewpoint. Random data synthesis and visible data reconstruction from low-dimensional hidden variables are shown, while classifier performance approaches that of a regularized discriminative network. Combination with a conventional discriminative CNN is also demonstrated.

: http://publica.fraunhofer.de/dokumente/N-637574.html