Image Data Enhancement Algorithm Based On Improved Generative Adversarial Networks

The volume and quality of data are important factors that influence model performance in deep learning.As a new type of unsupervised model,Deep Convolutional Generative Adversarial Network uses the adversarial learning idea of generator and discriminator to generate new image datasets,which solves the problem that traditional data enhancement methods cannot extract more image details,but this model has the problems of poor image quality and unstable model.In view of the above problems,this thesis improves the Deep Convolutional Generative Adversarial Network model from two aspects of external input noise dimension and internal structure,and proposes a Relativistic and Residual Deep Convolutional Generative Adversarial Network.The main work is as follows:The adaptive maximum likelihood dimension estimation algorithm is proposed to estimate the image intrinsic dimension in response to the negative bias phenomenon in the estimation of the image intrinsic dimension by the maximum likelihood dimension algorithm.By weighting and summing the intrinsic dimensions obtained by the maximum likelihood estimation algorithm and then taking the average value,the contribution of irrelevant data points is weakened,and the role of data points in important regions is strengthened.The optimal noise input dimension of the network is determined based on the results.The experimental results show that the use of the improved maximum likelihood dimension estimation algorithm to estimate the intrinsic dimension of the image can reduce the calculation amount of the model and improve the generation effect of the model.A Relativistic and Residual Deep Convolutional Generative Adversarial Network is proposed to address the problems of poor image quality and model collapse.Firstly,the SeLU activation function and the relative discriminator are used as the discriminator structure of the Generative Adversarial Network to enhance the quality and diversity of the generated images.And then,a residual block is introduced into the existing generator,which improves the ability of the model to capture image detail features while improving the stability of the model.Through experimental simulations on the MNIST,fashion-MNIST and MSTAR datasets,the results show that,compared with the Deep Convolutional Generative Adversarial Network,the FID of the improved algorithm in this thesis on the three datasets are reduced by 29.60%,18.71%,1.90%respectively,and the image data enhancement effect is significantly improved.