Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks

TL;DR

This paper introduces CAPGAN, a conditional variational autoencoder with balanced pre-training, to improve GANs for generating realistic minority class images in highly imbalanced datasets, outperforming existing methods.

Contribution

The paper proposes a novel CAPGAN model that combines conditional variational autoencoders with balanced pre-training to enhance GAN performance on imbalanced data.

Findings

CAPGAN outperforms state-of-the-art methods on multiple datasets.

It generates high-quality minority class samples with better metrics.

The method is effective on medical imaging datasets.

Abstract

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.