  Class imbalance occurs in many real-world applications, including image
classification, where the number of images in each class differs significantly.
With imbalanced data, the generative adversarial networks (GANs) leans to
majority class samples. The two recent methods, Balancing GAN (BAGAN) and
improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this
problem and restore the balance to the data. The former pre-trains the
autoencoder weights in an unsupervised manner. However, it is unstable when the
images from different categories have similar features. The latter is improved
based on BAGAN by facilitating supervised autoencoder training, but the
pre-training is biased towards the majority classes. In this work, we propose a
novel Conditional Variational Autoencoder with Balanced Pre-training for
Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate
realistic synthetic images. In particular, we utilize a conditional
convolutional variational autoencoder with supervised and balanced pre-training
for the GAN initialization and training with gradient penalty. Our proposed
method presents a superior performance of other state-of-the-art methods on the
highly imbalanced version of MNIST, Fashion-MNIST, CIFAR-10, and two medical
imaging datasets. Our method can synthesize high-quality minority samples in
terms of Fr\'echet inception distance, structural similarity index measure and
perceptual quality.
