  Interest in generative models has grown tremendously in the past decade.
However, their training performance can be adversely affected by contamination,
where outliers are encoded in the representation of the model. This results in
the generation of noisy data. In this paper, we introduce weighted conjugate
feature duality in the framework of Restricted Kernel Machines (RKMs). The RKM
formulation allows for an easy integration of methods from classical robust
statistics. This formulation is used to fine-tune the latent space of
generative RKMs using a weighting function based on the Minimum Covariance
Determinant, which is a highly robust estimator of multivariate location and
scatter. Experiments show that the weighted RKM is capable of generating clean
images when contamination is present in the training data. We further show that
the robust method also preserves uncorrelated feature learning through
qualitative and quantitative experiments on standard datasets.
