Ameliorated Deep Learning based on Improved Denoising Autoencoder and GACNN
Da Wang, Xuefeng Zhou, Zhihao Xu, Taobo Cheng, Xiaoxv Wang, Miao Haoqing · 2018
Improving the performance of deep learning and making it more in line with real life have always been the research direction of artificial intelligence. In this paper, a denoising autoencoder genetic algorithm convolution neural network (DGCNN) model based on deep learning is proposed. Two parts of the research work are combined to improve its performance. Firstly, the traditional autoencoder is replaced by the denoising autoencoder and improved the way of adding noise. Secondly, genetic algorithm is utilized to combine CNN at the stage of image classification. This allows DGCNN to cope with complex and volatile situations while enhancing image processing capabilities. Simulation results show that the method can enhance the ability of image processing than traditional methods. The performance of the proposed model is better than traditional method when the images of different loss levels are processed by this method. The results are verifying the feasibility and effectiveness of the model and algorithm. DGCNN shows better capacity in improving the performance of image processing and dealing with complex situations effectively.