Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification

TL;DR

This paper introduces MEDA_LUDE, a novel evolutionary algorithm-assisted deep learning method that improves imbalanced image classification by optimizing latent feature distributions for better image quality and diversity.

Contribution

It proposes a new distribution learning approach using an evolutionary algorithm and deep neural networks to enhance imbalanced image classification performance.

Findings

Effective in generating high-quality, diverse images

Improves classification accuracy on imbalanced datasets

Successfully applied to industrial fabric defect detection

Abstract

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.