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Deep active learning (DAL) studies the optimal selection of labeled data for training deep neural networks (DNNs). While data selection in traditional active learning is mostly optimized for given features, in DNN these features are learned and change with the learning process as well as the choices of DNN architectures. How is the optimal selection of data affected by this change is not well understood in DAL.

We present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh.

We present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space.