  Machine Learning has been applied in a wide range of tasks throughout the
last years, ranging from image classification to autonomous driving and natural
language processing. Restricted Boltzmann Machine (RBM) has received recent
attention and relies on an energy-based structure to model data probability
distributions. Notwithstanding, such a technique is susceptible to adversarial
manipulation, i.e., slightly or profoundly modified data. An alternative to
overcome the adversarial problem lies in the Generative Adversarial Networks
(GAN), capable of modeling data distributions and generating adversarial data
that resemble the original ones. Therefore, this work proposes to artificially
generate RBMs using Adversarial Learning, where pre-trained weight matrices
serve as the GAN inputs. Furthermore, it proposes to sample copious amounts of
matrices and combine them into ensembles, alleviating the burden of training
new models'. Experimental results demonstrate the suitability of the proposed
approach under image reconstruction and image classification tasks, and
describe how artificial-based ensembles are alternatives to pre-training vast
amounts of RBMs.
