Generative Enhancement of 3D Image Classifiers

Applied Sciences

Ján Jadlovský¹  Michal Varga¹  Slávka Jadlovská¹ 

[1] Department of Cybernetics and Artificial Intelligence, FEI TU of Košice, 04200 Košice, Slovakia;

关键词: generative modeling;    image classification;    convolutional neural network;    deep learning;    3D imaging;   

DOI  :  10.3390/app10217433

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown