Kun Fang1, 2, Jianquan Ouyang1, *
Journal on Artificial Intelligence, Vol.2, No.1, pp. 1-15, 2020, DOI:10.32604/jai.2020.09738
Abstract Generating an Adversarial network (GAN) has shown great development
prospects in image generation and semi-supervised learning and has evolved into TripleGAN. However, there are still two problems that need to be solved in Triple-GAN: based
on the KL divergence distribution structure, gradients are easy to disappear and training
instability occurs. Since Triple-GAN tags the samples manually, the manual marking
workload is too large. Marked uneven and so on. This article builds on this improved
Triple-GAN model (Improved Triple-GAN), which uses Random Forests to classify real
samples, automate tagging of leaf nodes, and use Least Squares Generative Adversarial
Networks (LSGAN) ideological… More >
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