基于半监督编码的深度卷积对抗网络模型研究

Research on Deep Convolutional Adversarial Network Model based onSemi-supervised Encoding

生成对抗网络因为在训练时具有能快速获取真实感、产生大量特征等优点,所以该方法在有监督和半监督的图像识别中逐渐得到广泛应用。文章以提高GAN(Generative Adversarial Networks)模型下的图像识别准确率为目标,基于已有的GAN模型,提出一种基于GAN模型的半监督深度学习模型,并将所建模型放入MNIST、CIFAR-10和Fashion-MNIST三种不同的数据集进行测试,结果显示,SSE-DCGAN模型在三种数据集标签数据较少时,能够很好地识别图像,在三个数据集上识别精度分别达到99.04%、83.66%、89.64%,进行消融实验的结果也表明,在模型中加入编码器后,准确率分别达到0.43%、2.55%、4.44%的提升。

Generative adversarial network has been widely used in supervised and semi-supervised image recognition due to its advantages of quickly acquiring realism and generating a large number of features during training.Aiming at improving the accuracy of image recognition under the General Adversarial Networks(GAN)model,this paper proposes a semi-supervised deep learning model based on the existing GAN model,and the proposed model is tested on three different datasets:MNIST,CIFAR-10,and Fashion-MNIST.The results show that the SSE-DCGAN model can effectively recognize images when there is less label data in the three datasets.The recognition accuracy reaches 99.04%,83.66%,and 89.64%on the three datasets,respectively.The results of ablation experiments also show that after an encoder is added to the model,the accuracy improves by 0.43%,2.55%,and 4.44%,respectively.