Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

By Maximilian Karl, Maximilian Sölch, Justin Bayer, Patrick van der Smagt. 2016

417 citations recorded by the source corpus.

We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction.

Subfields: Artificial Intelligence, Generative Models

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Citations in the corpus

Learning Latent Dynamics for Planning from Pixels (2018, methodology): “Karl et al. (2016) address mode collapse to a single future by restricting the transition function, (Moerland et al., 2017) focus on multi-modal transitions, and Doerr et al. (2018) stabilize training of purely stochastic models.”
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019, background): “Deep Variational Bayes Filters (DVBFs) (Karl et al., 2017) enhance the interpretability of DKFs by modelling state transitions with parametric – e.g. linear – models, which take in stochastic samples from the recognition model as inputs.”
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning (2017, methodology): “To draw a comparison with deep variational Bayes ﬁlters (DVBFs) [14], we apply the KVAE to [14]’s pendulum example.”
Disentangled Sequential Autoencoder (2018, background): “by combining deep neural networks with well-studied linear dynamical systems (Krishnan et al., 2015; Fraccaro et al., 2016; Karl et al., 2016; Johnson et al., 2016; Krishnan et al., 2017; Fraccaro et al., 2017), can further enhance the quality of the learned representations.”
Learning to Decompose and Disentangle Representations for Video Prediction (2018, methodology): “Our network models both motion and content probabilistically, and is regularized by learning transformations in a way similar to [16].”
State Representation Learning for Control: An Overview (2018, methodology): “This method is also used in [Karl et al., 2016] and [Krishnan et al., 2015].”
Dream to Control: Learning Behaviors by Latent Imagination (2019, background): “Learning effective latent dynamics models is becoming feasible through advances in deep learning and latent variable models (Krishnan et al., 2015; Karl et al., 2016; Doerr et al., 2018; Buesing et al., 2018).”
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning (2018, background): “for dynamical systems is typically aimed at improving reconstruction or forward prediction [18, 2, 41, 43], which largely benefits model-free RL and global model methods.”
Navigation and planning in latent maps (2018, background): “Several works were proposed recently that overcome the intractabilities of Bayes filters via approximate inference (Gu et al., 2015; Krishnan et al., 2015; Karl et al., 2016; Fraccaro et al., 2016; Maddison et al., 2017).”
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control (2019, background): “Karl et al. (2016) and Krishnan et al. (2017) adopt a variational approach and use recurrent architectures to learn state-space models from noisy observation.”
Z-Forcing: Training Stochastic Recurrent Networks (2017, background): “Several efforts have been made in order to bias the learning process towards parameter solutions for which the latent variables are used (Bowman et al., 2015; Karl et al., 2016; Kingma et al., 2016; Chen et al., 2017; Zhao et al., 2017).”
Probabilistic Recurrent State-Space Models (2018, neutral): “A variety of stochastic deep recurrent models has been presented based on Stochastic Gradient Variational Bayes (SGVB) (Bayer & Osendorfer, 2014; Krishnan et al., 2015; Watter et al., 2015; Chung et al., 2015; Archer et al., 2015; Karl et al., 2016; Fraccaro et al., 2016; Gemici et al., 2017).”
Deep Dynamical Modeling and Control of Unsteady Fluid Flows (2018, methodology): “Recent studies have worked toward learning state space models from image inputs for a variety of tasks, and have been designed to accommodate stochasticity in the dynamics and measurement noise [17], [26], [41].”
A Survey on Bayesian Deep Learning (2016, methodology): “Control Embed to Control [125] LV ✓ Deep Variational Bayes Filters [57] LV ✓ Probabilistic Recurrent State-Space Models [19] LV ✓ Deep Planning Networks [34] LV ✓”