  The key to overcome class imbalance problems is to capture the distribution
of minority class accurately. Generative Adversarial Networks (GANs) have shown
some potentials to tackle class imbalance problems due to their capability of
reproducing data distributions given ample training data samples. However, the
scarce samples of one or more classes still pose a great challenge for GANs to
learn accurate distributions for the minority classes. In this work, we propose
an Annealing Genetic GAN (AGGAN) method, which aims to reproduce the
distributions closest to the ones of the minority classes using only limited
data samples. Our AGGAN renovates the training of GANs as an evolutionary
process that incorporates the mechanism of simulated annealing. In particular,
the generator uses different training strategies to generate multiple offspring
and retain the best. Then, we use the Metropolis criterion in the simulated
annealing to decide whether we should update the best offspring for the
generator. As the Metropolis criterion allows a certain chance to accept the
worse solutions, it enables our AGGAN steering away from the local optimum.
According to both theoretical analysis and experimental studies on multiple
imbalanced image datasets, we prove that the proposed training strategy can
enable our AGGAN to reproduce the distributions of minority classes from scarce
samples and provide an effective and robust solution for the class imbalance
problem.
