Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement

In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to separately extract static and dynamic features from videos. Building sequential variational autoencoders with a two-stream architecture induces indu...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 5; S. 2515
Hauptverfasser:	Haga, Takeshi, Kera, Hiroshi, Kawamoto, Kazuhiko
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 24.02.2023 MDPI
Schlagworte:	adversarial training auxiliary adversarial classifier Bias Datasets Digital video Image processing inductive biases Machine learning Methods Neural networks Normal distribution Sensors sequential variational autoencoder Supervision Variables video disentanglement Japan adversarial training inductive biases sequential variational autoencoder video disentanglement auxiliary adversarial classifier
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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