Deep Learning Image Transfer by Simulation
Abdussalam Masaud Ammar, Amira Youssef Ellafi, Kenz Amhmed Bozed, Amer Ragab Zerek · 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA · 2021
The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning with Neural style transfer that has been a high risk application for deep learning, make attention from and advertising the effectiveness to both the academic prisons and the general public. However, we have found by removal experiments that optimizing an image in the way neural style transfer does, we can even factor out the deepness (multiple layers of exchange linear and nonlinear transformations) all together and have neural style transfer working to a certain range. We present the VGG-19 Artistic Type Neural Algorithm, which can convert and recombine the image quality and style of natural images. This algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well known portrait. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks (CNN) and demonstrate their potential for high level image synthesis and manipulation.