  Many real-world classification problems have imbalanced frequency of class
labels; a well-known issue known as the "class imbalance" problem. Classic
classification algorithms tend to be biased towards the majority class, leaving
the classifier vulnerable to misclassification of the minority class. While the
literature is rich with methods to fix this problem, as the dimensionality of
the problem increases, many of these methods do not scale-up and the cost of
running them become prohibitive. In this paper, we present an end-to-end deep
generative classifier. We propose a domain-constraint autoencoder to preserve
the latent-space as prior for a generator, which is then used to play an
adversarial game with two other deep networks, a discriminator and a
classifier. Extensive experiments are carried out on three different
multi-class imbalanced problems and a comparison with state-of-the-art methods.
Experimental results confirmed the superiority of our method over popular
algorithms in handling high-dimensional imbalanced classification problems. Our
code is available on https://github.com/TanmDL/SLPPL-GAN.
