Generative Deep-Neural-Network Mixture Modeling with Semi-Supervised MinMax+EM Learning

Nilay Pande, Suyash P. Awate

Abstract:

Deep neural networks (DNNs) for nonlinear generative mixture modeling typically rely on unsupervised learning
that employs hard clustering schemes, or variational learning
with loose / approximate bounds, or under-regularized modeling.
We propose a novel statistical framework for a DNN mixture
model using a single generative adversarial network. Our learning
formulation proposes a novel data-likelihood term relying on a
well-regularized / constrained Gaussian mixture model in the
latent space along with a prior term on the DNN weights. Our
min-max learning increases the data likelihood using a tight
variational lower bound using expectation maximization (EM). We
leverage our min-max EM learning scheme for semi-supervised
learning. Results on three real-world image datasets demonstrate
the benefits of our compact modeling and learning formulation
over the state of the art for nonlinear generative image (mixture)
modeling and image clustering.