382 research outputs found
Dynamic Variational Autoencoders for Visual Process Modeling
This work studies the problem of modeling visual processes by leveraging deep
generative architectures for learning linear, Gaussian representations from
observed sequences. We propose a joint learning framework, combining a vector
autoregressive model and Variational Autoencoders. This results in an
architecture that allows Variational Autoencoders to simultaneously learn a
non-linear observation as well as a linear state model from sequences of
frames. We validate our approach on artificial sequences and dynamic textures
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