Generative adversarial networks based on MLP

Hui Ding; Weifeng Chen; Bo Li; Zhifen He

doi:10.1117/12.2644522

12 October 2022 Generative adversarial networks based on MLP

Hui Ding, Weifeng Chen, Bo Li, Zhifen He

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123422Q (2022) https://doi.org/10.1117/12.2644522
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Inspired by the state-of-the-art performance of MLP-Mixer on image classification task, multilayer perceptron (MLP) model attracts a number of researcher's attention again. Although various MLP architectures have been proposed, most of them focus on image classification domain. In this paper, we extend MLP to the task of image generation based on generative adversarial network (GAN). The pure MLP model is not friendly to small dataset because it is a data-hungry architecture. Thus, we leverage a hybrid model to solve the problem which uses MLP blocks as generator and CNN blocks as discriminator. Experimental results demonstrate that our model outperforms the pure CNN network on CIFAR- 10 dataset in view of two different evaluation metrics. Besides, we apply eight widely popular MLP structures to our generator to validate which one achieves the most excellent performance in image generation task.

Citation Download Citation

Hui Ding, Weifeng Chen, Bo Li, and Zhifen He "Generative adversarial networks based on MLP", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123422Q (12 October 2022); https://doi.org/10.1117/12.2644522

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