Multiview Wasserstein generative adversarial network for imbalanced pearl classification

This work described in this paper aims to enhance the level of automation of industrial pearl classification through deep learning methods. To better extract the features of different classes and improve classification accuracy, balanced training datasets are usually needed for machine learning methods. However, the pearl datasets obtained in practice are often imbalanced; in particular, the acquisition cost of some classes is high. An enhanced generative adversarial network, named the multiview Wasserstein generative adversarial network (MVWGAN), is proposed for the imbalanced pearl classification problem. For the minority classes in the training datasets, the MVWGAN method can generate high-quality multiview images simultaneously to balance the original imbalanced datasets. The augmented balanced datasets are used to train a multistream convolution neural network (MS-CNN) for pearl classification. The experimental results show that MVWGAN can overcome the imbalanced learning problem and improve the classification performance of MS-CNN effectively. Moreover, feature visualization is implemented to intuitively explain the effectiveness of MVWGAN.

Automated summary

This work aims to enhance the automation level of industrial pearl classification through deep learning methods. To address the issue of imbalanced datasets, an enhanced generative adversarial network named MVWGAN is proposed to generate high-quality multiview images and balance the datasets. Experimental results demonstrate that the MVWGAN method effectively solves the imbalanced learning problem and improves the classification performance of pearl classification.