Generative Enhancement of 3D Image Classifiers.

In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network. [ABSTRACT FROM AUTHOR]