  Virtual Big Data (VBD) proved to be effective to alleviate mode collapse and
vanishing generator gradient as two major problems of Generative Adversarial
Neural Networks (GANs) very recently. In this paper, we investigate the
capability of VBD to address two other major challenges in Machine Learning
including deep autoencoder training and imbalanced data classification. First,
we prove that, VBD can significantly decrease the validation loss of
autoencoders via providing them a huge diversified training data which is the
key to reach better generalization to minimize the over-fitting problem.
Second, we use the VBD to propose the first projection-based method called
cross-concatenation to balance the skewed class distributions without
over-sampling. We prove that, cross-concatenation can solve uncertainty problem
of data driven methods for imbalanced classification.
