  We demonstrate in this paper that a generative model can be designed to
perform classification tasks under challenging settings, including adversarial
attacks and input distribution shifts. Specifically, we propose a conditional
variational autoencoder that learns both the decomposition of inputs and the
distributions of the resulting components. During test, we jointly optimize the
latent variables of the generator and the relaxed component labels to find the
best match between the given input and the output of the generator. The model
demonstrates promising performance at recognizing overlapping components from
the multiMNIST dataset, and novel component combinations from a traffic sign
dataset. Experiments also show that the proposed model achieves high robustness
on MNIST and NORB datasets, in particular for high-strength gradient attacks
and non-gradient attacks.
