To address these challenges,a handwritten digit generation method based on improved adversarial generation networks(GANs)is proposed.By introducing the generator and discriminator of GANs,an innovative network architecture was designed,and a series of training strategies were adopted to solve the instability and pattern collapse problems in GANs training.Through experiments using the MNIST dataset,the results showed that the new model achieved an accuracy of over 98％.Compared with traditional neural network methods,handwritten digit recognition systems based on improved ad-versarial generative networks have significant performance improvements.
关键词
数字识别/计算机视觉/神经网络/对抗生成网络
Key words
number recognition/computer vision/neural networks/adversarial generative net-work
维普
万方数据