  To address the trade-off problem of quality-diversity for the generated
images in imbalanced classification tasks, we research on over-sampling based
methods at the feature level instead of the data level and focus on searching
the latent feature space for optimal distributions. On this basis, we propose
an iMproved Estimation Distribution Algorithm based Latent featUre Distribution
Evolution (MEDA_LUDE) algorithm, where a joint learning procedure is programmed
to make the latent features both optimized and evolved by the deep neural
networks and the evolutionary algorithm, respectively. We explore the effect of
the Large-margin Gaussian Mixture (L-GM) loss function on distribution learning
and design a specialized fitness function based on the similarities among
samples to increase diversity. Extensive experiments on benchmark based
imbalanced datasets validate the effectiveness of our proposed algorithm, which
can generate images with both quality and diversity. Furthermore, the MEDA_LUDE
algorithm is also applied to the industrial field and successfully alleviates
the imbalanced issue in fabric defect classification.
