Journal of Advanced Information Technology and Convergence (한국정보기술학회 영문논문지)

Korean Institute of Information Technology (한국정보기술학회)
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Deep Adversarial Residual Convolutional Neural Network for Image Generation and Classification

  • Received : 2020.07.10
  • Accepted : 2020.07.28
  • Published : 2020.07.31
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Abstract

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NO.2017R1D1A1B04030870).

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